Information processing device, display device, and information processing program

Abstract

Develop an appropriate charging plan for electric vehicles. [Solution] The information processing device according to the embodiment has a controller that plans the charging schedule for multiple electric vehicles that are rented out. The controller acquires the driving route and location information of each of the multiple electric vehicles, predicts the destination of each electric vehicle from the driving route and location information of each electric vehicle, and plans the charging schedule for each electric vehicle based on the remaining battery charge of each electric vehicle at the time of return, which is calculated from the predicted distance to the destination of each electric vehicle.

Description

【Technical Field】 【0001】 The present invention relates to an information processing apparatus, a display apparatus, and an information processing program. 【Background Art】 【0002】 Conventionally, there is a service that rents out electric vehicles as rental cars. In such a service, electric vehicles are charged at the business offices of the operators who provide the rental cars. For example, Patent Document 1 discloses a technique for formulating a charging plan for each electric vehicle based on the scheduled return time, scheduled rental time, and the number of charging facilities of the electric vehicle. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2021-78248 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 <s However, in the prior art, since the remaining battery level at the time of return varies depending on the driving route of the electric vehicle, there are cases where the electric vehicle cannot be fully charged even if it is charged according to the charging plan. Therefore, there was room for improvement in formulating an appropriate charging plan for electric vehicles. 【0005】 The present invention has been made in view of the above, and an object thereof is to provide an information processing apparatus, a display apparatus, and an information processing program capable of formulating an appropriate charging plan for an electric vehicle. 【Means for Solving the Problems】 【0006】 To solve the above-mentioned problems and achieve the objective, the information processing device according to the present invention has a controller that plans the charging schedule for multiple electric vehicles that are rented out. The controller acquires the driving route and location information of each of the multiple electric vehicles, predicts the destination of each electric vehicle from the driving route and location information of each electric vehicle, and plans the charging schedule for each electric vehicle based on the remaining battery charge of each electric vehicle at the time of return, which is calculated from the predicted distance to the destination of each electric vehicle. [Effects of the Invention] 【0007】 According to the present invention, the estimated travel distance calculated from the predicted destination of the electric vehicle is used to estimate the remaining battery charge at the time of return, thereby enabling accurate estimation of the remaining battery charge at the time of return. Therefore, according to the present invention, an appropriate charging plan for the electric vehicle can be formulated. [Brief explanation of the drawing] 【0008】 [Figure 1] Figure 1 is an explanatory diagram showing an overview of the information processing performed by the information processing device according to the embodiment. [Figure 2] Figure 2 is a block diagram showing an example configuration of an information processing device according to the embodiment. [Figure 3] Figure 3 shows an example of data stored in the vehicle data database. [Figure 4] Figure 4 shows an example of data stored in the user data database. [Figure 5] Figure 5 is an explanatory diagram of the prediction process for the predicted destination of movement. [Figure 6] Figure 6 is an explanatory diagram of the prediction process for the predicted destination of movement. [Figure 7] Figure 7 is an explanatory diagram showing an example of the operating state of an electric vehicle. [Figure 8] Figure 8 is an explanatory diagram showing an example of a charging schedule. [Figure 9] Figure 9 is an explanatory diagram showing an example of a charging schedule. [Figure 10]Figure 10 is an explanatory diagram showing an example of a charging schedule. [Figure 11] Figure 11 is a flowchart showing an example of a process performed by an information processing device. [Modes for carrying out the invention] 【0009】 Hereinafter, embodiments of the information processing device, display device, and information processing program will be described in detail with reference to the attached drawings. However, the present invention is not limited to the embodiments described below. The information processing device according to the embodiment is an information processing device that plans and provides a charging plan that enables efficient charging of multiple electric vehicles to a rental car operator (hereinafter sometimes simply referred to as "operator") that rents out electric vehicles to customers. 【0010】 Figure 1 is an explanatory diagram showing an overview of the information processing performed by the information processing device 1 according to the embodiment. When the information processing device 1 plans a charging schedule for electric vehicles 10, it first receives data transfers related to the electric vehicles 10 from on-board units 20 installed in multiple electric vehicles 10 that are to be rented out. For example, the information processing device 1 receives data transfers from the on-board units 20 of the electric vehicles 10 at predetermined intervals, including information such as the driving route and location information, and the remaining charge level. 【0011】 As shown in Figure 1, the information processing device 1 has a vehicle data DB (database) 31 and a user data DB (database) 32. The vehicle data DB 31 is a database that manages driving route and location information, as well as remaining charge, etc., received from each electric vehicle 10. 【0012】 In addition, the user data DB 32 is a database that manages information of users (customers of rental cars) who use the electric vehicle 10. For example, the information regarding the users registered in the user data DB 32 includes the user information input by the users when they reserve the electric vehicle 10 and the reservation information of the electric vehicle 10 by the users. The user information includes the user's name, age, address, etc., and the reservation information includes information such as the business office where the electric vehicle 10 is to be rented, the scheduled rental time, and the scheduled return time. Note that the user data DB 32 may manage information regarding the movement history of the electric vehicle 10 for each user. 【0013】 When a user makes a reservation for the electric vehicle 10 through a rental car operator system (hereinafter referred to as "operator system"), the reservation information reserved by the user is provided from the operator system to the information processing device 1. 【0014】 For example, the reservation information includes the user's information, the location where the user rents out the electric vehicle 10, the scheduled rental time, the location where the user returns the electric vehicle 10, the scheduled return time, etc. In addition, the reservation information may include options such as designation of the vehicle type (or number of passengers) and presence or absence of a child seat. 【0015】 The electric vehicle 10 is an electric automobile. The electric vehicle 10 may be any vehicle that runs by an electric motor, such as an electric motorcycle and a small electric mobility. Also, the in-vehicle device 20 is an in-vehicle device that can measure, for example, the running position, running distance, and remaining battery level of the electric vehicle 10 on which it is mounted, such as a drive recorder or a car navigation device equipped with GPS (Global Positioning System). The remaining battery level may be measured by the electric vehicle 10. In this case, the in-vehicle device 20 acquires the remaining battery level from the electric vehicle 10. 【0016】 Here, the running distance is the running distance from when the electric vehicle 10 is lent to the user and leaves the operator's parking lot until the current time. Also, the remaining battery level is the remaining power level of the battery that serves as the power source for running the electric vehicle 10. 【0017】 Here, the electric vehicle 10 is a vehicle lent to the general public. For example, when the electric vehicle 10 is a commercial vehicle such as a bus, since it is operated according to a prior operation plan, it is possible to formulate a charging plan for the electric vehicle 10 according to the operation plan. In contrast, when the electric vehicle 10 is lent to the general public, since the destination and the like are different for each user, there is a variation in the driving distance until the electric vehicle 10 is returned, so it is difficult to formulate a charging plan compared to a commercial vehicle. 【0018】 Therefore, when formulating a charging plan for the electric vehicle 10 that operates irregularly, the information processing device 1 predicts the remaining charge amount when the electric vehicle 10 is returned, and formulates a charging plan for each electric vehicle 10 based on the predicted remaining charge amount. 【0019】 That is, the information processing device 1 according to the embodiment can accurately estimate the charging time required to charge the battery of the returned electric vehicle 10 by predicting the remaining charge amount when the electric vehicle 10 is returned. 【0020】 Thereby, the information processing device 1 according to the embodiment can formulate an appropriate charging plan for the electric vehicle 10. In particular, the information processing device 1 according to the embodiment can appropriately formulate a charging plan for the electric vehicle 10 lent to the general public. 【0021】 Specifically, as shown in FIG. 1, when formulating a charging plan, the information processing device 1 first performs a movement prediction for each electric vehicle 10 (step S1). The movement prediction is performed based on the data stored in the vehicle data DB31. 【0022】 Specifically, the movement prediction is a process of predicting the predicted movement destination to which the electric vehicle 10 to be predicted will move from the current driving route and position information of the electric vehicle 10 to be predicted based on the past movement history stored in the vehicle data DB31. 【0023】 For example, the information processing device 1 predicts the next destination that the electric vehicle 10 will visit from its current location. For example, the information processing device 1 predicts multiple locations that the electric vehicle 10 will visit in chronological order during the period until it is returned, each of which is a predicted destination. Note that these locations can be any place, such as a tourist spot, an intersection, or a convenience store. 【0024】 Furthermore, the information processing device 1 may utilize user data stored in the user data DB 32 to predict destinations. For example, the information processing device 1 may extract the travel history of users similar to the user of the electric vehicle 10 whose movement is to be predicted from the user data DB 32, and then refer to the extracted users' travel history to predict the destinations of the electric vehicle 10 whose movement is to be predicted. 【0025】 Specifically, the information processing device 1 extracts the movement history of users of the electric vehicle 10 whose movement is to be predicted, as well as the movement history of users with similar user information, from the user data DB 32, and predicts the destination of the electric vehicle 10 whose movement is to be predicted based on the movement history of the extracted users. 【0026】 In this process, the information processing device 1 extracts the movement history of users with similar attributes to the user of the electric vehicle 10 whose movement is to be predicted from the user data DB 32. Note that the user attributes here include concepts such as demographic attributes and psychographic attributes. 【0027】 In this way, the information processing device 1 can improve the accuracy of the movement prediction by referring to the past movement history of users with similar attributes to the user of the electric vehicle 10 whose movement is to be predicted. Note that the movement prediction in step S1 is not limited to the above process, and for example, weather information or event information may be used as variables. Specifically, the information processing device 1 may extract movement history from the vehicle data DB 31 during weather similar to the current weather, or extract movement history during events similar to the current event information, and then predict the destination. This allows the information processing device 1 to predict the destination considering the weather and surrounding events. Therefore, the information processing device 1 can improve the accuracy of the movement prediction. The specific logic of the movement prediction will be described later with reference to Figures 5 and 6. 【0028】 Next, the information processing device 1 calculates the predicted travel distance to the destination predicted in step S1 (step S2). The predicted travel distance is calculated by determining the travel distance from the current location of the electric vehicle 10 to the predicted destination predicted in the travel prediction. The predicted travel distance is the predicted total travel distance that the electric vehicle 10 will travel to the business office where it will be returned. 【0029】 In other words, in calculating the predicted travel distance in step S2, the information processing device 1 calculates the total travel distance from the current location of the electric vehicle 10, through each predicted destination, to the business office as the predicted travel distance. 【0030】 Subsequently, the information processing device 1 formulates a charging plan based on the predicted driving distance calculated in step S2 (step S3). Specifically, the information processing device 1 predicts the remaining charge level when the electric vehicle 10 is returned to the business premises after traveling the predicted driving distance, based on the current remaining charge level and the predicted driving distance. 【0031】 The information processing device 1 then calculates the predicted charging time for each electric vehicle 10 using the predicted remaining charge when the electric vehicle 10 is returned to the business premises. The predicted charging time is the charging time required to ensure that the electric vehicle 10 has enough charge to be charged from the time it is returned until it is rented out again. 【0032】 The information processing device 1 devises a charging plan such that the predicted charging time for each electric vehicle 10 falls within the period from the scheduled return time when each electric vehicle 10 is to be returned to the scheduled rental time when the next rental takes place. 【0033】 For example, the charging plan is formulated based on conditions such as the predicted charging time of the electric vehicle 10, the rental time of the electric vehicle 10, the scheduled return time, the scheduled time for the next rental, the location of the vehicle, the location of the charger, the proximity of the electric vehicle 10 to the charger, and the charging priority. 【0034】 Subsequently, the information processing device 1 generates a charging plan table 6 based on the devised charging plan and notifies the terminal device 51 of the rental car operator 50 (step S4). 【0035】 The terminal device 51 is, for example, a smartphone used by the rental car company 50. The terminal device 51 may be any device equipped with communication functions used by the rental car company 50, such as a tablet, laptop, or desktop computer. 【0036】 The terminal device 51 is equipped with a display device 52. The display device 52 displays the charging schedule 6 received from the information processing device 1. When the rental car operator 50 confirms the charging schedule 6, it sends a confirmation of the contents back to the information processing device 1 (step S5). 【0037】 The display device 52 of the terminal device 51 can appropriately provide the charging schedule 6 to the workers of the business by displaying the charging schedule 6 received from the information processing device 1. 【0038】 In this way, the information processing device 1 uses the location information of the electric vehicle 10 obtained from the in-vehicle unit 20 to calculate the predicted destination and predicted distance traveled by the electric vehicle 10, and predicts the remaining charge of the electric vehicle 10 when it is returned. 【0039】 As a result, the information processing device 1 according to the embodiment can accurately predict the estimated charging time of the electric vehicle 10 at the time of return, and thus an appropriate charging plan for the electric vehicle 10 can be formulated. 【0040】 Next, an example of the configuration of the information processing device 1 according to the embodiment will be described with reference to Figure 2. Figure 2 is a block diagram showing an example of the configuration of the information processing device 1 according to the embodiment. As shown in Figure 2, the information processing device 1 comprises a communication unit 2, a storage unit 3, and a controller 4. 【0041】 The communication unit 2 is a communication interface that communicates information between the in-vehicle unit 20, the terminal device 51, and the operator system (not shown) via a communication network such as the Internet. 【0042】 The storage unit 3 is, for example, an information storage device such as a data flash, and has the vehicle data DB31 and user data DB32 shown in Figure 1. The vehicle data DB31 is a database that manages the travel route, location information, etc., of each electric vehicle 10. 【0043】 Here, we will explain an example of the data stored in the vehicle data DB31 using Figure 3. Figure 3 is a diagram showing an example of the data stored in the vehicle data DB31. As shown in Figure 3, the vehicle data DB31 stores information such as "vehicle ID," "user ID," "travel history," and "remaining charge" in a manner that associates them with each other. 【0044】 The "Vehicle ID" field stores an identifier for identifying each electric vehicle 10. The "User ID" field stores an identifier for identifying the user who used the electric vehicle 10 identified by the corresponding vehicle ID. 【0045】 The "Movement History" field stores the movement history of the electric vehicle 10, identified by the corresponding vehicle ID, when used by the user identified by the corresponding user ID. The movement history includes the current location information of the electric vehicle 10, the current driving route, and their history. The "Battery Level" field stores the current battery level of the electric vehicle 10, identified by the corresponding vehicle ID. 【0046】 Next, we will explain an example of data stored in the user data DB32 using Figure 4. Figure 4 is a diagram showing an example of data stored in the user data DB32. As shown in Figure 4, the user data DB32 stores information on items such as "User ID," "User Information," "Reservation Information," and "Travel History" in a manner that associates them with each other. 【0047】 The "User ID" field stores an identifier to identify each user. This User ID is the same as the User ID shown in Figure 3. The "User Information" field stores user information for the user identified by the corresponding User ID. For example, the user information is the information registered by the user when reserving the electric vehicle 10, and includes information such as name, age (date of birth), and address. The user information may also include information about other demographic attributes and psychographic attributes. The information processing device 1 may estimate the psychographic attributes based on the spots the user has visited when using the electric vehicle 10 in the past. 【0048】 The "Reservation Information" field stores reservation information for the electric vehicle 10 made by the user identified by the corresponding user ID. The reservation information includes information such as the scheduled rental time and scheduled return time of the electric vehicle 10. The "Movement History" field stores the movement history of the electric vehicle 10 used by the user identified by the corresponding user ID. The movement history is updated according to location information and other data transmitted from the on-board unit 20 of the electric vehicle 10. 【0049】 Returning to the explanation of Figure 2, let's describe Controller 4. Controller 4 includes a microcomputer with a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), and various circuits. Controller 4 functions by the CPU executing an information processing program stored in ROM, using RAM as a working area. The information processing program may also be stored in a storage device via an external communication line or the like. 【0050】 Furthermore, the controller 4 may be composed of hardware such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), either partially or entirely. 【0051】 Controller 4 plans the charging schedule for the multiple electric vehicles 10 that are to be rented out. First, Controller 4 periodically (for example, every minute) acquires the driving route, location information, and battery level of the electric vehicle 10 equipped with each on-board unit 20. Controller 4 registers the acquired driving route, location information, and battery level of the electric vehicle 10 in the vehicle data DB 31. Controller 4 also sequentially receives reservation information for the electric vehicles 10 from the operator system and registers it in the user data DB 32. 【0052】 Next, the prediction process performed by the controller 4 will be explained. In the prediction process, the controller 4 predicts the predicted destination of each electric vehicle 10 based on the travel route and location information of each electric vehicle 10, and calculates the predicted distance to the predicted destination of each electric vehicle 10. 【0053】 For example, the controller 4 predicts the destination of the electric vehicle 10 based on the travel route and location information received from the electric vehicle 10 that is currently on loan. In this case, the controller 4 predicts the destination of the electric vehicle 10 that is currently on loan based on the past travel history of the electric vehicle 10 stored in the vehicle data DB 31. 【0054】 Specifically, the controller 4 uses the past travel history of the electric vehicle 10 to train a model (AI; ​​Artificial Intelligence) on the movement transitions between each spot. This model predicts the next destination of the electric vehicle 10 when its travel route and current location information are input. 【0055】 In this way, the controller 4 predicts the destination of the currently rented electric vehicle 10 based on its past travel history, and can therefore predict a statistically high-probability location as the destination. 【0056】 Furthermore, when training the model, the controller 4 may, for example, use user information as training data to train the model. In this case, the model predicts the next destination of the electric vehicle 10 when it receives input such as the electric vehicle 10's travel route, current location information, and user information about the electric vehicle 10's driver. 【0057】 For example, the user information here refers to information about the user's attributes. This information includes demographic and psychographic attributes. In this way, the controller 4 can predict the destination based on the user's attributes, thereby improving the accuracy of the destination prediction. 【0058】 Furthermore, when training the model, the controller 4 may use, for example, weather information or event information as training data. In this case, the model will predict the next destination of the electric vehicle 10 when it is given the electric vehicle 10's travel route, current location information, weather information, and event information as input. 【0059】 In other words, the controller 4 can improve the accuracy of predicting the destination by taking into account weather information and event information in addition to the driving route and location information of the electric vehicle 10. 【0060】 Here, using Figures 5 and 6, we will explain a specific example of the movement prediction process performed by the controller 4. Figures 5 and 6 are explanatory diagrams of the movement prediction process. Figure 5 shows spots A1 to A5, and it is assumed that the rented electric vehicle 10 is at spot A1. 【0061】 The controller 4 calculates the probability of each electric vehicle 10 moving from spot A1 to the next spot based on the past behavioral transitions of each electric vehicle 10 stored in the vehicle data DB 31. For example, the probability of moving to each spot can be calculated using various methods such as transition probability, Markov chain, and hidden Markov model. 【0062】 In the example shown in Figure 5, there is a 70% probability that the electric vehicle 10 will move from spot A1 to spot A2, and a 30% probability that it will move to spot A4. Therefore, the controller 4 predicts spot A2, which has a higher probability of movement, as the predicted destination. 【0063】 In fact, if the electric vehicle 10 moves from spot A1 to spot A2, the controller 4 will select the next destination from spots A1, A3 to A5, starting from spot A2, as shown in Figure 6. 【0064】 In the example shown in Figure 6, the probability of spot A3 being the destination of spot A2 is 50%, which is the highest, so controller 4 selects spot A3 as the predicted destination for spot A2. By repeating these processes, controller 4 predicts that the electric vehicle 10 will move in the order of spot A1 → spot A2 → spot A3 → spot A5... For example, controller 4 repeats these processes to predict each destination that the electric vehicle 10 will pass through before returning to the business premises. 【0065】 Furthermore, for example, if the actual destination of the electric vehicle 10 deviates from the predicted destination, the controller 4 corrects the predicted destination. Specifically, for example, if the electric vehicle 10 moves from spot A1 to spot A4, which has a lower probability than spot A2, the controller 4 predicts the destination starting from spot A4. 【0066】 In this way, the controller 4 corrects the predicted destination based on the location information of the electric vehicle 10, thereby appropriately correcting the predicted destination of the electric vehicle 10. 【0067】 The controller 4 uses these processes to predict the destination of the electric vehicle 10 and calculates the predicted distance to that destination. The controller 4 then predicts the remaining charge of the electric vehicle 10 when it is returned. 【0068】 The remaining charge is calculated by subtracting the power consumption until the electric vehicle 10 is returned from the current remaining charge. The power consumption until the vehicle is returned is calculated by multiplying the estimated distance traveled by the electric vehicle 10 until it is returned by the power consumption per unit distance. 【0069】 The controller 4 calculates the remaining charge of the electric vehicle 10 upon its return, and then calculates the predicted charging time based on the remaining charge at the time of return. The predicted charging time is calculated by dividing the remaining charge at the time of return by the amount of charge supplied per unit time at the charging facility. 【0070】 The controller 4 then formulates a charging plan that efficiently charges multiple electric vehicles 10 based on the reservation information and predicted charging time of each electric vehicle 10. The controller 4 then creates a charging plan table 6 in accordance with the formulated charging plan and transmits it to the terminal device 51. 【0071】 Next, with reference to Figures 7 to 10, an example of a charging schedule 6 created by the controller 4 will be explained. Here, we will explain using the example of a case where five electric vehicles 10, designated as vehicles 11 to 5, are in operation. Furthermore, we will explain using the example of a case where two chargers, designated as unit 1 61 and unit 2 62, are installed at the facility. 【0072】 This section will first describe an example of the operating state of the electric vehicle 10 when creating a charging schedule, and then describe an example of the charging schedule 6 created at that time. Figure 7 is an explanatory diagram showing an example of the operating state of the electric vehicle 10. Figures 8 to 10 are explanatory diagrams showing examples of the charging schedule 6. 【0073】 As shown in Figure 7, when creating the charging schedule, it is assumed that vehicles 11-3 (13) have been returned to the business premises by the start of business hours (for example, 8:00). It is also assumed that vehicles 4-5 (14) will be returned at the end of the morning. 【0074】 In this case, the controller 4 creates a charging schedule 6, which associates the charger, the vehicle to be charged, the charging start time, the charging end time, and the charging time (in minutes), as shown in Figure 8, for example. The controller 4 then transmits the created charging schedule 6 to the terminal device 51 for display. This allows the workers at the facility to adhere to the charging schedule 6 created by the controller 4 by moving the vehicle to be charged according to the schedule described in the charging schedule 6. 【0075】 Specifically, the worker can connect the returned vehicle No. 1 (11) to unit No. 1 (61) at 8:00 and begin charging, and then connect the returned vehicle No. 2 (12) to unit No. 2 (62) at 8:30 and begin charging. After that, when charging of vehicle No. 1 (11) is completed at 9:30, the worker can connect the returned vehicle No. 3 (13) to unit No. 1 (61) at 10:00 and begin charging. 【0076】 Then, when the charging of vehicle 3 13 is completed at 13:30, the worker can connect vehicle 4 14, which will be returned by the end of the morning, to unit 1 61 at 13:30 and begin charging. Subsequently, when vehicle 5 15 is returned by the end of the morning, since the charging of vehicle 2 12 by unit 2 62 has already been completed at 13:00, vehicle 5 15 can be connected to unit 2 62 and begin charging. In this way, the workers at the facility can efficiently charge multiple electric vehicles 10. 【0077】 Furthermore, the controller 4 can also create a charging schedule 6, for example, as shown in Figure 9, which adds the charging schedule for vehicles 11 to 515, charged by units 11 and 262, to the timetable from the start to the end of business hours at the business premises. 【0078】 At this time, the controller 4 creates a charging schedule table 6 by adding time bars corresponding to the charging time of each electric vehicle 10 to the timetable. Then, the controller 4 transmits the created charging schedule table 6 to the terminal device 51 for display. 【0079】 This allows workers at the facility to intuitively recognize visually which electric vehicle 10 should be charged, when, and with which charger, enabling efficient charging of multiple electric vehicles 10. 【0080】 In Figure 9, a charging schedule is added to the timetable from the start to the end of business hours at the business premises. However, it is not necessary to complete charging by the end of business hours, and there may be electric vehicles 10 that remain connected to the charger from the end of business hours until the start of the next business hours. In addition, the controller 4 may create a timetable based on the start and end times of business hours at the business premises. 【0081】 Furthermore, the controller 4 may use the prescribed working hours of the workers at the workplace (for example, 9 hours including break time) as a timetable. If it is possible to eliminate waiting times for workers at the workplace or waiting times for the electric vehicle 10 to charge by making the break start and end times changeable, a timetable with changed break start and end times can also be created. In addition, if it is more efficient and possible to advance the start and end times of work on the following day than to extend the end time of work on the current day, the start and end times of work on the workplace can be changed and notified to the workers. 【0082】 Furthermore, the controller 4 can also create a charging plan table 6, for example, as shown in Figure 10. The charging plan table 6 is a table that illustrates the sequence of operations for moving each electric vehicle 10, the movement route of each electric vehicle 10, and the movement route of the electric vehicle 10 after charging, on a map that includes the installation locations of chargers at the business site and rectangular parking spaces. 【0083】 The controller 4 then transmits the created charging schedule 6 to the terminal device 51 for display. At this time, the information processing device 1 may be configured to transmit the charging schedule 6 not only to the terminal device 51 used by the workers at the business site, but also to the terminal device 51 of the user who is renting the electric vehicle 10. 【0084】 When the controller 4 transmits the charging plan table 6 to the terminal device 51 of the user who has rented the electric vehicle 10, it formulates a charging plan including the designated parking location for the electric vehicle 10 to be returned. The controller 4 then notifies the user (driver) of the electric vehicle 10 of the parking location. 【0085】 This allows the rental car company 50 to have users who rent the electric vehicle 10 park it in a designated parking spot. Therefore, the rental car company 50 can offer a new service where users do not have to bring the key back to the reception desk when they leave, for example, by installing a key return box for the electric vehicle 10 in the designated parking spot. 【0086】 Furthermore, when the controller 4 transmits the charging schedule 6 to the terminal device 51 used by the workers at the business site, it formulates a charging plan that includes the movement schedule and route of the electric vehicle 10 from the designated parking position to the charging position, and from the charging position to the standby position after charging. 【0087】 The information processing device 1 then notifies the worker (driver) of the electric vehicle 10's travel schedule and route. At this time, the information processing device 1 guides the worker along the electric vehicle 10's route using voice or images. 【0088】 When the worker receives notification of the charging schedule 6, which includes the movement schedule and route of the electric vehicle 10, the worker moves vehicles 11 to 515 according to the route guidance from the information processing device 1 and the charging schedule 6, and performs the movement and charging work for the electric vehicle 10. 【0089】 For example, if the charging schedule table 6 shown in Figures 8 to 10 has been created, at the start of operations, vehicle 11 is parked in parking space A-1. Vehicle 22 is parked in parking space A-5. Vehicle 33 is parked in parking space A-2. 【0090】 Therefore, the charging operator will connect vehicle 11, parked in parking space A-1, to unit 61 at 8:00 and begin charging (first operation). Next, the charging operator will connect vehicle 2, parked in parking space A-5, to unit 62 at 8:30 and begin charging (second operation). 【0091】 Next, at 9:30, once charging of vehicle 11 is complete, the moving worker moves vehicle 11 to the waiting position, parking space E-1 (waiting / moving operation). After that, the charging worker moves vehicle 313 from parking space A-2 to parking space A-1, and at 10:00 connects vehicle 311 to unit 11 and begins charging (third operation). 【0092】 When charging of vehicle 2, unit 12 is completed at 13:00, the mobile operator will move vehicle 2, unit 12 to the standby parking space E-2 (standby movement operation). Also, when charging of vehicle 3, unit 13 is completed at 10:30, the mobile operator will move vehicle 3, unit 13 to the standby parking space E-3 (standby movement operation). 【0093】 Afterward, the charging operator will have the user park vehicle 14 (No. 4), which will be returned by the end of the morning, in parking space A-2 (Fourth Task). Note that the fourth task may also be performed by the charging operator. Then, at 13:30, the charging operator will move vehicle 14 (No. 4) from parking space A-2 to A-1, connect it to unit 61, and begin charging. 【0094】 Afterward, the charging operator will have the user park vehicle 15 (No. 5), which will be returned by the end of the morning, in parking space A-5 (fifth task). Note that the charging operator may also perform the fifth task. Then, at 14:00, the charging operator will connect vehicle 15 (No. 5) to unit 62 and begin charging. 【0095】 When charging of vehicle 4, 14 is completed at 15:00, the mobile operator will move vehicle 4, 14 to the standby parking space E-4 (standby movement operation). Also, when charging of vehicle 5, 15 is completed at 16:00, the mobile operator will move vehicle 5, 15 to the standby parking space E-5 (standby movement operation). 【0096】 In this way, the operator can move vehicles 11 to 515 according to the charging plan to charge each electric vehicle 10, and then move each electric vehicle 10 to its standby position after charging to complete the charging work. In addition, the rental car operator 50 can reduce unnecessary movement work and travel time for the electric vehicles 10 from the start of charging to the standby position, thereby extending the operating time of the electric vehicles 10. 【0097】 Furthermore, the information processing device 1 creates a charging plan table 6 that maps (visualizes) the designated parking spaces in the parking lot into blocks, allowing the operator to intuitively understand the parking location and movement route of the vehicle to be charged. 【0098】 Furthermore, when defining a parking position, the information processing device 1 uses latitude information, longitude information, and polygon information to define each parking position by enclosing its four corners. Alternatively, the information processing device 1 may be configured to define each parking position by defining rectangles with radii of several meters from the latitude and longitude of the center point of the parking position. 【0099】 Next, with reference to Figure 11, the processes executed by the information processing device 1 according to this embodiment will be described. Figure 11 is a flowchart of an example of the processes executed by the information processing device 1. Note that the processes shown below are repeatedly executed by the controller 4 of the information processing device 1. 【0100】 As shown in Figure 11, the controller 4 acquires vehicle information from the on-board unit 20 of the rented electric vehicle 10 (step S101). The vehicle information includes the vehicle ID, driving route, location information, and battery level of the electric vehicle 10. For example, the controller 4 acquires vehicle information from the on-board unit 20 installed in the rented electric vehicle 10 at predetermined intervals. 【0101】 Next, the controller 4 predicts the destination of the electric vehicle 10 based on the acquired vehicle information (step S102). The prediction of the destination is performed by calculating the probability of the next candidate location to which the electric vehicle 10 will move, starting from the spot where the electric vehicle 10 is currently located, based on the electric vehicle 10's past movement history. 【0102】 For example, the controller 4 predicts multiple destinations that the electric vehicle 10 will travel to before it is returned. That is, the controller 4 predicts each of the multiple intermediate stops that the electric vehicle 10 will pass through before it is returned as a predicted destination. 【0103】 Next, the controller 4 calculates the predicted travel distance based on the predicted destinations (step S103). The predicted travel distance is the distance the electric vehicle 10 travels through each predicted destination until it is returned to the operator. The controller 4 maps each predicted destination onto the map information and calculates the predicted travel distance as the route taken through these destinations until the vehicle is returned to the operator. 【0104】 Next, the controller 4 calculates the remaining charge of the electric vehicle 10 at the time of return based on the predicted mileage (step S104). The remaining charge at the time of return is calculated by subtracting the power consumption until the electric vehicle 10 is returned from the current remaining charge. The power consumption until the return is calculated by multiplying the predicted distance traveled by the electric vehicle 10 until it is returned by the power consumption per unit distance. 【0105】 Next, the controller 4 calculates the required charging time for the electric vehicle 10 based on the calculated remaining charge (step S105), updates the charging plan table 6 based on the calculated charging time (step S106), and then terminates the process. 【0106】 In the embodiment described above, the information processing device 1 was described in which it estimates the remaining battery level of the electric vehicle 10 at the time of return according to the predicted destination and predicted mileage of the electric vehicle 10, but it is not limited to this. For example, the information processing device 1 may estimate the remaining battery level of the electric vehicle 10 at the time of return by taking into consideration weather conditions and the usage status of the electric vehicle 10's air conditioner, etc. 【0107】 The information processing device 1 estimates that, for example, when the air conditioner of the electric vehicle 10 is used, such as in summer and winter, power is consumed not only for driving but also for operating the air conditioner, so the remaining battery level will be lower than during spring and autumn when the air conditioner is not used. As a result, the information processing device 1 can calculate the remaining battery level more accurately, and thus formulate a more efficient and appropriate charging plan. 【0108】 If multiple electric vehicles 10 are parked in the business premises parking lot and waiting to be charged, the information processing device 1 will start charging the electric vehicles 10 in the order of the pre-set priority. In this case, the information processing device 1 will, for example, set a higher priority for electric vehicles 10 that have a shorter time until their next rental. 【0109】 Furthermore, the information processing device 1 may be configured to set a higher priority for electric vehicles 10 that are popular for rental. Also, the information processing device 1 may be configured to set a higher priority for electric vehicles 10 with shorter charging times. Furthermore, the information processing device 1 may be configured to set a higher priority for electric vehicles 10 with longer charging times. Also, the information processing device 1 may be configured to set a higher priority for electric vehicles 10 with a remaining charge of 80% or less. 【0110】 Further effects and modifications can be readily derived by those skilled in the art. Therefore, broader aspects of the present invention are not limited to the specific details and representative embodiments expressed and described above. Accordingly, various modifications are possible without departing from the spirit or scope of the overall concept of the invention as defined by the appended claims and their equivalents. [Explanation of symbols] 【0111】 1. Information Processing Device 2 Communications Department 3 Storage section 4 controllers 6. Charging Schedule 10 Electric Vehicles 20 Onboard equipment 31 Vehicle Data Database 32 User Data DB 50 Rental car companies 51 Terminal device 52 Display device

Claims

[Claim 1] It has a controller that plans the charging schedule for multiple electric vehicles that are rented out, The controller acquires the travel route and location information of each of the plurality of electric vehicles, Based on the driving route and location information of each of the electric vehicles, predict the destination of each of the electric vehicles. A charging plan for each of the electric vehicles is formulated based on the remaining battery charge at the time of return of each electric vehicle, which is calculated from the predicted distance traveled to the predicted destination of each electric vehicle. Information processing device. [Claim 2] The aforementioned electric vehicle is These are vehicles that are leased to general users. The information processing apparatus according to claim 1. [Claim 3] The aforementioned controller, The predicted destination is corrected according to the position information of the electric vehicle. The charging plan is formulated according to the corrected predicted travel distance. The information processing apparatus according to claim 1. [Claim 4] The aforementioned controller, Based on the reservation information for each of the aforementioned electric vehicles, the charging plan is formulated. The charging plan is formulated based on the scheduled return time of each of the electric vehicles and the scheduled rental time for the next rental. The information processing apparatus according to claim 1. [Claim 5] The aforementioned controller, Based on the past travel history of each of the electric vehicles, predict the predicted destination of each electric vehicle. The information processing apparatus according to claim 1. [Claim 6] The aforementioned controller, The predicted destination is determined according to the attributes of the user who rents out the electric vehicle. The information processing apparatus according to claim 5. [Claim 7] The aforementioned controller, The predicted destination is predicted based on at least one of the surrounding event information and the surrounding weather information. The information processing apparatus according to claim 1. [Claim 8] The system acquires the driving route and location information for each of the multiple electric vehicles being rented out. Based on the driving route and location information of each of the electric vehicles, predict the destination of each of the electric vehicles. The charging plan for each of the electric vehicles is received and displayed from an information processing device that plans the charging plan for each electric vehicle based on the remaining charge of each electric vehicle at the time of return, calculated from the predicted distance to the predicted destination for each electric vehicle. Display device. [Claim 9] An information processing program executed by a controller that plans the charging schedule for multiple electric vehicles that are rented out, A procedure for acquiring the driving route and location information of each of the aforementioned multiple electric vehicles, A procedure for predicting the predicted destination of each of the electric vehicles from the driving route and location information of each of the electric vehicles, A procedure for formulating a charging plan for each of the electric vehicles based on the remaining charge of each electric vehicle at the time of return, calculated from the predicted distance to the predicted destination for each electric vehicle, An information processing program that includes this.

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