JP5542284B2 - Vehicle use support device - Google Patents

Description

  The present invention relates to a vehicle and a vehicle usage support apparatus that predicts a vehicle usage state based on daily behavior of a vehicle and a user of the vehicle, and supports the user's usage of the vehicle.

  Conventionally, for example, a charge / discharge management apparatus as shown in Patent Document 1 below is known. This conventional charge / discharge management device is a charge / discharge reward information receiving means for receiving from a charge management central server charge / discharge reward information that defines a reward that can be enjoyed by a customer's charge / discharge behavior and restrictions on the implementation of charge / discharge; A calculation unit for creating a charge / discharge plan including a total charge amount in a certain time period and a total discharge amount and an estimated use start time of the electric vehicle so that the reward is maximized based on the charge / discharge reward information, and charge / discharge Charge / discharge command transmission means for instructing the electric vehicle to start / end charge / discharge according to the plan, charge / discharge amount monitoring means for monitoring charge / discharge, and identification of charge / discharge implementation content and itself Charge / discharge execution result transmitting means for transmitting the charge / discharge execution result including the individual identification information to the charge management central server is provided. As a result, the energy consumption performance and social environment performance of the entire power system are improved while taking into account the behavior of consumers.

  Conventionally, for example, an electric vehicle charging system and an electric vehicle charging method as shown in Patent Document 2 below are also known. In this conventional electric vehicle charging system and electric vehicle charging method, when the electric vehicle stops in the parking space, the control device 10 communicates with the electric vehicle via the changeover switch to acquire the vehicle ID, the battery remaining amount, the operation information, and the like. The customer's stay time specified from the vehicle ID is predicted using a method such as a neural network, the charge time is calculated from the remaining battery level and the charging speed of the charger, and charging is performed so that the charge time falls within the stay time. Create a schedule. Further, in this conventional electric vehicle charging system and electric vehicle charging method, if the calculated charging time does not fit in the stay time, the charging amount required for the return trip is calculated from the operation information and the charging time is recalculated. When the amount of charge is insufficient, an alarm indicating the fact or a message prompting to extend the departure time is output. Thereby, a plurality of electric vehicles can be charged efficiently.

  Furthermore, conventionally, for example, a power management system disclosed in Patent Document 3 below is also known. This conventional power management system includes a secured power amount calculation unit, which learns the power consumption amount of one trip acquired by the travel history acquisition device and necessary basic power amount for normal traveling. Furthermore, based on the weather information from the weather information detection unit, the necessary power for use of the air conditioner or the like is added to determine the reserved power amount. Thereby, the electric power corresponding to a driving | running | working environment can be ensured for a vehicle-mounted battery, and it can respond also to a sudden going out.

JP 2010-81722 A JP 2011-83165 A Japanese Patent No. 4164996

  By the way, in the conventional apparatus and system shown by the said patent documents 1-3, a travel start time and a travel start distance are estimated from the past electric power demand. However, when the user intends to use the vehicle with more convenience and certainty, it is necessary to predict the use situation of the vehicle with higher accuracy in consideration of the user's daily behavior (lifestyle). is there.

  The present invention has been made in order to cope with the above-described problems, and an object of the present invention is to predict a vehicle usage situation for each vehicle or each user and supply information reflecting the predicted vehicle usage information. The object is to provide a vehicle use support device.

  In order to achieve the above object, a feature of the present invention is that vehicle usage is provided that includes usage status prediction means for predicting the usage status of a vehicle by a user, and supports the usage of the vehicle by the user based on the predicted usage status of the vehicle. In the support device, the usage status prediction unit acquires information on a past usage status of the vehicle for each vehicle or for each user who uses the vehicle, and based on the acquired information, a predetermined parking is performed in the future. Information regarding the departure time starting from the position and information regarding the arrival time arriving at the predetermined parking position are predicted and determined for each vehicle or each user using the vehicle, and the acquired information and the predicted departure are determined. Based on the information on the time, the travel of the vehicle that departed in the departure time zone including the departure time represented by the information on the predicted departure time in the future Information relating to separation is predicted and determined for each vehicle or each user using the vehicle, and one of the information related to the predicted departure time, the information related to the predicted arrival time, and the information related to the predicted travel distance It is intended to predict the usage status of the vehicle for each vehicle or each user using the vehicle, and to provide the user with information reflecting the predicted usage status of the vehicle. In this case, the usage status prediction unit acquires and inputs information on the past usage status of the vehicle for each vehicle or for each user who uses the vehicle, and the information acquired by the input unit. Based on the above, time for predicting and determining information on departure time starting from a predetermined parking position and information on arrival time arriving at the predetermined parking position for each vehicle or each user using the vehicle Based on the prediction means, the information acquired by the input means and the information on the predicted departure time, the vehicle that departed in the departure time zone including the departure time represented by the information on the predicted departure time will travel in the future. Travel distance prediction means for predicting and determining information related to travel distance for each vehicle or each user using the vehicle, and the time prediction Using each vehicle or the vehicle using any one of the information related to the departure time predicted by the stage, the information related to the arrival time predicted by the time prediction means, and the information related to the travel distance predicted by the travel distance prediction means It is also possible to provide an output unit that predicts the vehicle usage situation for each user to be provided and provides the user with information reflecting the predicted vehicle usage situation.

  In this case, the information on the usage status of the past vehicle includes departure time information indicating a departure time from the predetermined parking position, arrival time information indicating an arrival time at the predetermined parking position, and the departure time information. Is the travel distance information representing the travel distance from the departure from the predetermined parking position at the departure time represented by the information, the information regarding the predicted departure time is the departure represented by the departure time information Information representing the cumulative probability of occurrence of the time, the information on the predicted arrival time is information representing the cumulative probability of arrival of the arrival time represented by the arrival time information, and the information on the predicted travel distance is Information representing the cumulative probability of appearance of the travel distance represented by the travel distance information in the departure time zone including the departure time having the appearance probability of departure time When may. In this case, the information on the usage status of the past vehicle input and acquired by the input means includes departure time information indicating a departure time from the predetermined parking position, and an arrival time at the predetermined parking position. The arrival time information that represents, and the travel distance information that represents the travel distance since the departure from the predetermined parking position at the departure time represented by the departure time information. The information on the departure time is information indicating the cumulative probability of appearance of the departure time represented by the departure time information, and the information on the arrival time predicted by the time prediction means is the arrival time represented by the arrival time information. Information representing the cumulative probability of appearance, and the information related to the travel distance predicted by the travel distance prediction means includes a departure time having a cumulative probability of appearance of the departure time. May is information indicating the appearance cumulative probability of the travel distance represented by the travel distance information in the band.

  Further, in this case, the usage state predicting means determines at least the vehicle to determine the cumulative probability of appearance to be secured against the cumulative probability of appearance of the departure time or the cumulative probability of appearance of the arrival time. In consideration of the safety probability set by the user who uses the vehicle, the vehicle usage situation for each vehicle or each user who uses the vehicle is predicted, and information reflecting the predicted vehicle usage situation is provided to the user. be able to. In this case, the output means determines at least the appearance cumulative probability to be secured with respect to the appearance cumulative probability of the departure time predicted by the time prediction means or the appearance cumulative probability of the arrival time. Therefore, in consideration of the safety probability set by the user using the vehicle, the vehicle usage status for each vehicle or each user using the vehicle is predicted, and the information reflecting the predicted usage status of the vehicle Can be provided to the user.

  In these cases, the information on the predicted travel distance is, for example, at least one of a statistical frequency method, a non-parametric method, and a parametric method. It is possible to calculate the cumulative appearance probability of the travel distance represented by the travel distance information in the departure time zone including the departure time having the appearance cumulative probability. In this case, the mileage prediction means uses the information on the predicted mileage by using, for example, at least one of a statistical frequency method, a nonparametric method, and a parametric method. Thus, it can be calculated as the cumulative occurrence probability of the travel distance represented by the travel distance information in the departure time zone including the departure time having the appearance probability of the departure time. In this case, the statistical parametric method represents, for example, a distribution of the cumulative cumulative probability of the travel distance represented by the travel distance information in the departure time zone including the departure time having the cumulative probability of appearance of the departure time. A statistical model that gives a prior distribution to the parameters of the statistical model can be used.

  Another feature of the present invention is that the information reflecting the predicted use state of the vehicle is information relating to charge / discharge control for controlling charging or discharging of a battery mounted on the vehicle. In this case, the output means can output information reflecting the predicted use state of the vehicle as information on charge / discharge control for controlling charging or discharging of a battery mounted on the vehicle.

  In this case, for example, the use state prediction unit may determine, for each time, by the time based on the information on the power consumption of the battery mounted on the vehicle in the past for each vehicle or each user who uses the vehicle. Create a rule curve that represents the relationship of the estimated required power amount that requires collateral, determine the charging start time to the battery or the discharge stop time from the battery using the created rule curve, Information relating to charge / discharge control for controlling charging or discharging of the batteries mounted on the both in accordance with the charging start time or the discharge stop time determined by estimation can be provided. In this case, the utilization status prediction means is further based on information on the power consumption of the battery mounted on the vehicle in the past for each vehicle or each user using the vehicle, which is input and acquired by the input means. And a rule curve creating unit that creates a rule curve that represents a relationship of estimated required electric energy that must be secured by that time for each time, and the output unit is created by the rule curve creating unit Batteries mounted on both in accordance with the charging start time or the discharge stop time determined by estimating and determining the charging start time or discharging stop time from the battery using a rule curve It is possible to provide information on charge / discharge control for controlling charging or discharging of the battery.

  In this case, for example, the utilization state predicting means is in contact with the rule curve in the vicinity of the local maximum value in the rule curve using the created rule curve, or always based on the estimated required power amount by the rule curve. Is set to the latest charge start time that is the latest of the charge start times to the battery, or the latest time of the discharge stop time from the battery. Charging / discharging for controlling charging or discharging of the batteries mounted on both according to the latest charging start time or the latest discharging stop time determined by estimating and determining the latest discharging stop time Information about control can be provided. In this case, the output means uses the rule curve created by the rule curve creation means, contacts the rule curve in the vicinity of the local maximum value in the rule curve, or always needs to be estimated by the rule curve. A line segment having a value larger than the amount is set, and the latest charge start time which is the latest time among the charge start times to the battery, or the latest time among the discharge stop times from the battery For estimating and determining the latest discharge stop time, and for controlling charging or discharging of the batteries mounted on both according to the latest charge start time or the latest discharge stop time determined by the estimation Information on charge / discharge control can be provided.

  Further, in this case, the utilization state predicting means, for example, determines the line segment for estimating and determining the latest charging start time, the chargeable amount to the battery, the expected temperature at the time of charging the battery, The line segment is set using at least one of a charging voltage supplied to the battery and a charging current supplied to the battery, or the line segment for estimating and determining the latest discharge stop time is the battery. Can be set using at least one of the dischargeable amount by the above, the expected temperature at the time of discharging the battery, the discharge voltage supplied from the battery, and the discharge current supplied from the battery. In this case, the output means supplies the line segment for estimating and determining the latest charging start time to the battery chargeable amount, the expected temperature when the battery is charged, and the battery. The line segment for setting and determining the latest discharge stop time is set using at least one of a charging voltage to be supplied and a charging current to be supplied to the battery, the dischargeable amount by the battery, It can be set using at least one of the predicted temperature when the battery is discharged, the discharge voltage supplied from the battery, and the discharge current supplied from the battery.

  Furthermore, another feature of the present invention is that the information reflecting the predicted use state of the vehicle is information related to car sharing in which the vehicle is shared by a plurality of users. In this case, the output means can output the information reflecting the predicted use state of the vehicle as information relating to car sharing in which the vehicle is shared by a plurality of users.

  In this case, the use state predicting means uses, for example, information on the departure time and information on the arrival time determined and predicted for each vehicle or for each user who uses the vehicle. It is possible to extract a vehicle that can be used among them and provide the extracted vehicle as information related to the car sharing. In this case, the output means uses a plurality of vehicles by using the information on the departure time and the information on the arrival time, which are predicted and determined for each vehicle or each user who uses the vehicle by the time prediction means. The vehicle that can be used is extracted, and the extracted vehicle can be provided as information related to the car sharing.

  According to these, the information on the departure time and the information on the arrival time for each vehicle or each user who uses the vehicle, the travel distance of the vehicle that departed in the departure time zone, and the information on the past vehicle usage status of the vehicle. Can be used to predict with extremely high accuracy. Therefore, it is possible to predict and grasp the usage status of the vehicle with extremely high accuracy. In this way, by accurately predicting the usage status of the vehicle, for example, the charging of the battery mounted on the vehicle or the discharging from the battery can be reliably completed by the time when the user uses the vehicle. In addition, it is possible to provide information related to a vehicle that can be reliably used in a condition (for example, time zone) desired by the user in car sharing in which a plurality of users share and use the vehicle.

It is the schematic which shows the structure of the vehicle utilization assistance apparatus which concerns on this invention. It is a functional block diagram showing the function implement | achieved by computer program processing in the electronic control unit of FIG. FIG. 3 is a diagram for explaining an estimated departure time map and an estimated arrival time map for a vehicle A created by a departure / arrival time prediction map generation unit in FIG. 2. FIG. 3 is a diagram for explaining an estimated departure time map and an estimated arrival time map for a vehicle B created by a departure / arrival time prediction map generation unit in FIG. 2. It is a figure for demonstrating the predicted travel distance map classified by departure time zone regarding the vehicle A produced by the travel distance prediction map production | generation part of FIG. It is a figure for demonstrating the predicted travel distance map classified by departure time zone regarding the vehicle B produced by the travel distance prediction map production | generation part of FIG. It is a figure for demonstrating the travel distance accumulation frequency distribution according to each time slot | zone approximated with the Weibull distribution. It is a figure for demonstrating the rule curve showing the estimation required electric energy (required battery remaining amount) with respect to each time regarding the vehicle used as the object of charging / discharging control output from the data output part of FIG. It is a figure for demonstrating determination of an estimated latest charge start time and an estimated latest discharge stop time.

Hereinafter, a vehicle utilization support device 10 (hereinafter also simply referred to as “the present device 10”) according to an embodiment of the present invention will be described with reference to the drawings.

  The apparatus 10 acquires and accumulates daily behavior patterns for each vehicle or for each user who uses the vehicle, and accurately predicts the future usage status of the vehicle based on this behavior pattern. That is, for each vehicle or each user, the device 10 departs from the predetermined parking position where the vehicle is parked, arrives at the predetermined parking position, and departs from the predetermined parking position. The distance traveled by the vehicle before reaching (returning) the position is collected and accumulated as a daily behavior pattern (lifestyle) using the vehicle. The device 10 accurately predicts the daily usage situation (more specifically, the usage situation for each day of the week) for each vehicle from the behavior patterns for each vehicle or each user collected and accumulated in this way. . Moreover, this apparatus 10 provides various information with respect to the user of a vehicle based on the utilization condition for every vehicle estimated in this way accurately.

  Here, any vehicle may be used as a vehicle for which the usage status is predicted by the apparatus 10, but in the present embodiment, the vehicle is a so-called rechargeable vehicle (specifically, an external charging device). An electric vehicle (EV) or a plug-in hybrid vehicle (PHV) that charges a battery mounted using a stand is adopted and implemented. In addition, in the vehicle that is the target of the use situation prediction by the device 10, the departure time starting from the parking position and the arrival arriving at the parking position start from a predetermined parking position where an available charging stand is installed. It is assumed that the time can be detected and the travel distance traveled from the time of departure from the parking position to the return (before arrival) can be detected. In this case, any method can be adopted for detecting the departure time, arrival time, and mileage. For example, each vehicle is equipped with a known navigation unit, and this navigation unit is the current vehicle. By detecting the position, the departure time, arrival time, and travel distance may be detected starting from the predetermined parking position. Or it is also possible for this apparatus 10 to detect (acquire) the departure time, arrival time, and travel distance of a vehicle by acquiring the present position of a vehicle directly via known communication.

  Then, in the vehicle that is subject to the usage status prediction by the device 10, the identification information (hereinafter referred to as “this”) set in advance for identifying the vehicle with respect to the departure time, arrival time, and travel distance detected as described above. The identification information is referred to as “vehicle ID”) and is output to the apparatus 10 in a state of being associated with each other. In addition, the vehicle that is the target of the usage status prediction by the apparatus 10 includes a well-known authentication unit that authenticates a user who uses the vehicle. The authentication means may be any method such as authenticating a pre-registered electronic key used by the user or biometrically authenticating the user who has boarded the vehicle with a camera mounted on the vehicle. When the user can be authenticated in this way, the vehicle authenticates the authenticated user (hereinafter, this authentication information) with respect to the detected departure time, arrival time, travel distance, and vehicle ID. "User ID") is also output to the apparatus 10 in a state of being associated with each other. For this reason, the vehicle includes a known communication means that enables communication with the apparatus 10 by wire or wireless.

  As schematically shown in FIG. 1, the apparatus 10 includes an electronic control unit 11, a communication unit 12, and a storage unit 13 that are connected to be communicable with each other as a minimum necessary configuration. The electronic control unit 11 is a microcomputer whose main components are a CPU, a ROM, a RAM, and the like, and comprehensively controls the operation of the apparatus 10 by executing various programs. The communication unit 12 acquires various types of information such as departure time, arrival time, travel distance, vehicle ID, and user ID detected from the vehicle side by wire or wireless. As for the installation location of the device 10, when the device 10 is installed in the vicinity of a predetermined parking position where the vehicle is parked, the communication unit 12 is connected directly from the vehicle side by wired communication or wireless communication. When the apparatus 10 is set far away from a predetermined parking position where the vehicle is parked, the communication unit 12 is connected to, for example, the Internet communication network (public line network) or dedicated communication. The various types of information can be acquired from the vehicle side by wired or wireless communication via a network.

  The storage unit 13 includes a storage medium such as a hard disk and a semiconductor memory, and a drive device for the storage medium. Programs and data necessary for the electronic control unit 11 to comprehensively control the operation of the apparatus 10 are stored in advance. I remember it. In the storage unit 13, a vehicle ID database 13a, a user ID database 13b, and a vehicle travel history database 13c are constructed. The vehicle ID database 13a searches for a vehicle ID (for example, a vehicle registration number, a chassis number, etc.) set in advance as described above in order to identify the vehicle (a plurality of vehicles) for which the apparatus 10 predicts the usage situation. I remember it. The user ID database 13b is a user ID (for example, an electronic key registration number or biometric authentication information) necessary for the above-described authentication in order to identify the user (plural users) who uses the vehicle for which the apparatus 10 predicts the usage status. Etc.) are stored so as to be searchable. The vehicle travel history database 13c integrally includes departure time information representing departure time, arrival time information representing arrival time, and travel distance information representing travel distance supplied from the vehicle side via the communication unit 12. By associating with vehicle ID or / and user ID, it memorize | stores for every vehicle or every user.

  Next, the operation of the apparatus 10 configured as described above will be described with reference to the functional block diagram of FIG. 2 showing functions realized by computer program processing in the electronic control unit 11. The electronic control unit 11 includes a data input unit 31, a departure / arrival time prediction map generation unit 32, a travel distance prediction map generation unit 33, and a data output unit 34.

  Now, in order to facilitate the following description, a situation is assumed in which a certain user A among a plurality of users uses a certain vehicle A among a plurality of vehicles that the apparatus 10 predicts a utilization situation. In this case, when the user A gets on the vehicle A, the user A who gets on the vehicle A is authenticated, and the corresponding user ID is stored in a predetermined storage unit together with the own vehicle ID. When the user A starts traveling (departs) the vehicle A from a predetermined parking position, the navigation unit mounted on the vehicle A determines that the vehicle A is predetermined based on the detected change in the current position of the vehicle A. It is detected that the vehicle has departed from the parking position, and departure time information indicating the time when the departure was detected (that is, departure time) is stored in a predetermined storage unit together with the vehicle ID and the user ID. The navigation unit mounted on the vehicle A calculates the travel distance from the departure of the vehicle A based on the change in the current position of the detected vehicle A. Is stored in a predetermined storage unit together with the vehicle ID, the user ID and the departure time information. Further, the navigation unit mounted on the vehicle A detects that the vehicle A has arrived at a predetermined parking position based on a change in the current position of the detected vehicle A, and the time when the arrival is detected (ie, arrival) Arrival time information representing (time) is stored in a predetermined storage means together with the vehicle ID, user ID, departure time information and travel distance information. When the vehicle A is parked at a predetermined parking position, the device 10 stores the vehicle ID, the user ID, the departure time information, the arrival time information, and the travel distance information stored in the storage unit via the communication unit. Output to.

  In the present apparatus 10, the electronic control unit 11 receives the vehicle ID, user ID, departure time information, arrival time information, and travel distance information output from the vehicle A that has finished traveling via the communication unit 12. Then, the electronic control unit 11 specifies the vehicle A by searching the vehicle ID database 13a using the output vehicle ID and searches the user ID database 13b using the output user ID. Is identified. Subsequently, the electronic control unit 11 stores the output departure time information, arrival time information, and travel distance information in the travel history database 13c. At this time, the electronic control unit 11 stores the departure time information, the arrival time information, and the travel distance information for each day of the week so as to be searchable in a storage position previously formed for each vehicle and each user in the travel history database 13c. Remember.

  As described above, in the state where the travel history database 13c of the storage unit 13 is distinguished for each vehicle or for each user and departure time information, arrival time information and travel distance information are stored for each day of the week, the electronic control unit 11 inputs departure time information, arrival time information, and travel distance information from the travel history database 13c. More specifically, the data input unit 31 is, for example, from Monday to Friday, which is a weekday, for the vehicle A among the departure time information, arrival time information, and travel distance information stored in the travel history database 13c. Departure time information, arrival time information, and travel distance information can be selectively input. Alternatively, the data input unit 31 includes, for example, the departure time information, arrival time information, and travel distance information stored in the travel history database 13c. Time information and mileage information can be selectively input. In this case, as the input departure time information, arrival time information, and travel distance information of the vehicle A, each information when the vehicle A is used by a plurality of users including the user A is input.

  The data input unit 31 includes, for example, departure time information and arrival time information on Monday to Friday, which is a weekday, for the user A among the departure time information, arrival time information, and travel distance information stored in the travel history database 13c. And travel distance information can be selectively input. Alternatively, the data input unit 31 includes, for example, Saturday and Sunday, which is a holiday for the user A, among the departure time information, the arrival time information, and the travel distance information stored in the travel history database 13c. Time information and mileage information can be selectively input. In this case, as the departure time information, arrival time information, and travel distance information of the user A to be input, each information when a plurality of vehicles including the vehicle A are used is input.

  Thus, for example, when the departure time information for each day of the week, the arrival time information, and the travel distance information for the vehicle A or the user A are input, the data input unit 31 departs for each day of the input information. The time information and the arrival time information are output to the departure / arrival time prediction map generation unit 32, and the travel distance information is output to the travel distance prediction map generation unit 33. If the departure time information, arrival time information, and travel distance information for each day of the week are associated with the vehicle ID, the use status of the vehicle A will be described as described below, and the departure time information, arrival time information, and travel distance information for each day of the week. Is associated with the user ID, the usage status of the user A is obtained in the same manner as in the case of the vehicle A described below.

ただし、前記式１中のtjは、データ入力部３１によって入力された出発時刻情報が表す出発時刻のうちの早い方からｊ番目の出発時刻を表すものであり、又、データ入力部３１によって入力された到着時刻情報が表す到着時刻のうちの早い方からｊ番目の到着時刻を表すものである。そして、前記式１中のP_tjは、出発時刻tj(j=0,…n)又は到着時刻tj(j=0,…n)の出現累積確率（安全確率）を表す。又、前記式１中のN_tjは、出発時刻tj(j=0,…n)又は到着時刻tj(j=0,…n)における度数を表し、N_Allは、出発時刻tj(j=0,…n)又は到着時刻tj(j=0,…n)における合計度数を表す。 The departure / arrival time prediction map generation unit 32 uses the following formula 1 to create a future departure time map for weekdays and holidays, and a future arrival time map for weekdays and holidays.

However, tj in Equation 1 represents the j-th departure time from the earlier of the departure times represented by the departure time information input by the data input unit 31, and is input by the data input unit 31. The j-th arrival time from the earliest arrival time represented by the received arrival time information. P _{tj in} Equation 1 represents the accumulated probability (safety probability) of departure time tj (j = 0,... N) or arrival time tj (j = 0,... N). N _{tj in} Equation 1 represents the frequency at departure time tj (j = 0,... N) or arrival time tj (j = 0,... N), and N _All represents departure time tj (j = 0 ,... N) or the total frequency at arrival time tj (j = 0,... N).

  Then, for example, a predicted weekday and holiday departure time map and estimated arrival time map predicted for the vehicle A in accordance with the equation 1 are as shown in FIG. FIG. 4 is a weekday and holiday estimated departure time map and estimated arrival time map for vehicles B other than vehicle A. Comparing these maps, the departure time and arrival time of the vehicles A and B are clearly different. Specifically, for example, when attention is paid to the departure time of a flat sunrise indicated by a solid line in FIGS. 3 and 4, the vehicle A has a high probability of starting in a certain time zone (for example, 8 o'clock), but the vehicle B It can be understood that although there is a high probability of departure in some areas, the departure is almost complete. As described above, when the predicted departure time map and the predicted arrival time map are created, the departure / arrival time predicted map generation unit 32 supplies the generated predicted departure time map and estimated arrival time map to the data output unit 34, and the vehicle. With respect to A, the predicted departure time time indicating the predicted departure time from the prepared predicted departure time map is supplied to the travel distance prediction map generation unit 33.

ただし、前記式２中のP_hdは、出発時間帯hと、この出発時間帯hにおける距離d（以下、「距離時間帯d」と称呼する。）の出現累積確率（安全確率）を表す。又、前記式２中のN_hdは、出発時間帯hと距離時間帯dの度数を表し、N_hは、出発時間帯hの合計度数を表す。 The travel distance prediction map generation unit 33 inputs the travel distance information for each day of the week for the vehicle A from the data input unit 31 and the predicted departure time information from the departure / arrival time prediction map generation unit 32. Then, the travel distance prediction map generation unit 33 uses the travel distance information and the predicted departure time information to predict the longest travel distance in the departure time zone (that is, departure time) according to the following formula 2, and the departure by weekdays and holidays. Create an estimated mileage map by time zone.

However, P _hd in the formula 2 represents the accumulated probability (safety probability) of the departure time zone h and the distance d in the departure time zone h (hereinafter referred to as “distance time zone d”). N _{hd in} Equation 2 represents the frequency of the departure time zone h and the distance time zone d, and N _h represents the total frequency of the departure time zone h.

  Then, for example, the predicted travel distance map (for example, 8 o'clock, 9 o'clock, 10 o'clock) for each day of normal sunrise predicted for the vehicle A according to Equation 2 is as shown in FIG. FIG. 6 is a predicted travel distance map for a vehicle B other than the vehicle A according to the time of departure from normal sunrise. Comparing these maps, the travel distance in the departure time zone is clearly different between the vehicle A and the vehicle B. Specifically, for example, when attention is paid to 9 o'clock and 10 o'clock, which are the time zones when the morning sun starts, vehicle A has a probability of traveling a long distance, but vehicle B travels a relatively short distance. It can be understood that there is a high probability of

Here, with respect to the vehicle A, according to the normal sunrise forecast time map shown in FIG. 3 created by the departure / arrival time prediction map generation unit 32, as described above, for example, the probability of starting at 8 o'clock is a probability. high. That is, with respect to the vehicle A, a large amount of data represented by the frequency in the distance time zone d, that is, the travel distance information is collected until 8 o'clock. Less. On the other hand, the vehicle B has a high probability of starting evenly as described above according to the predicted sunrise / sunrise predicted time map shown in FIG. 4 created by the departure / arrival time predicted map generation unit 32. That is, with respect to the vehicle B, a relatively large amount of frequency in the distance time zone d around 8:00, that is, travel distance information is collected. In this case, since the appearance cumulative probability (safety probability) P _hd calculated according to the formula 2 depends on N _hd, that is, the frequency of the departure time zone h and the distance time zone d, for example, the frequency becomes extremely small (or In a situation where the frequency is “0”), for example, a map having an unnatural distribution may be generated, such as 9 o'clock and 10 o'clock in FIG.

By the way, a distribution of a quantity that takes a positive value such as a travel distance is often represented by an exponential distribution family such as an exponential distribution, a Weibull distribution, a gamma distribution, or a lognormal distribution. In addition, when expressing such a distribution, it is also effective to express it as a mixed normal distribution or the like. For this reason, in creating the predicted travel distance map for each departure time zone, N pieces of data (t ₁ , d ₁ ), (t ₂ , d) that record that the vehicle has started at time t and traveled a distance d are now recorded. ₂ ),..., (T _N , d _N ) are assumed, and a simultaneous probability density function f (d, t) of time t and distance d is created from this data. Note that the simultaneous probability density function f (d, t) at time t and distance d is the probability density f (t) starting at time t and the conditional probability density that travels the distance d when departing at time t. It can also be expressed as the product f (d, t) = f (d | t) f (t) of the function f (d | t). As a method for determining f (d, t) or f (t) and f (d | t) from data, there are three types of methods as follows. Here, when using these methods, it is effective to perform processing for each classification after appropriately classifying data in consideration of environmental variables such as day of the week, season, temperature, and weather. Furthermore, it is also effective to collectively represent parameters for various environments using these environment variables as qualitative variables, for example, using a generalized linear model.

a. Frequency method The frequency method divides data at certain time steps (for example, one hour), and represents a cumulative distribution of data represented by travel distance information in each time zone in a table. This frequency method is effective in that the cumulative distribution of the travel distance data in each time zone can be represented by a table, so that an arbitrary distribution can be maintained by reducing the calculation load. However, if the number of data is small, for example, as shown in FIGS. 5 and 6, only a stair-like coarse distribution can be obtained, the distribution cannot be determined in a time zone in which no data exists, or information is stored in a small number of parameters. There is a demerit such that comparison with other distributions and aggregation becomes difficult because it cannot be aggregated.

b. Non-parametric method a. In the frequency method, it is possible to adopt a non-parametric method that makes no assumptions about the distribution type of the population as a method to eliminate the fact that a stair-like coarse distribution is obtained when the number of data is small. is there. In this case, for example, by adopting a method that does not assume a specific distribution form such as kernel density estimation, an arbitrary distribution can be obtained smoothly. However, even when such a non-parametric method is employed, there is a demerit such that it is difficult to compare and aggregate with other distributions because information cannot be aggregated into a small number of parameters.

c. Parametric method b. In contrast to the non-parametric method, it is possible to adopt a parametric method in which some assumption is made in advance for the distribution type of the population. Specifically, in this parametric method, the distribution f (d | t) in the mileage direction is represented by an exponential distribution family such as exponential distribution, Weibull distribution, gamma distribution, parametric probability such as lognormal distribution, mixed Gaussian distribution, etc. It is expressed as a distribution, and its parameters are determined every time step (every time step). Thereby, a smooth distribution can be obtained in the travel distance direction. In this case, it is also effective to represent the sum as a plurality of distributions. In this parametric method, the parameter according to the prior distribution can be determined even if the number of data is small or “0” (zero) by giving the parameter prior distribution. Here, regarding the prior distribution, for example, when a large amount of parameter information relating to many vehicles or users is accumulated, the distribution of these parameters may be a prior distribution. As a result, by aggregating the parameters for each predetermined group, it is possible to give a rational parameter prior distribution to the new group whose data is still “0” (zero). Parameters are modified slowly as data in the population increases.

そして、前記式３をa₀，…，a₂₃について最大化することによって、例えば、図７に示すように、パラメータを決定することができる。 Specifically will be illustrated below, for example, f (d | t) = g (d; a t) and so that, the parameter (vector) a 24-hour time period using a distribution represented by _t classification to the case of obtaining the at each time zone (every hour) is, a _0, ..., prior distribution _{p (a 0, ..., a} 23) of a ₂₃ think of. In this case, the probability (likelihood) L that N pieces of data are observed from this parameter can be expressed by Equation 3 below.

Then, by maximizing Equation 3 with respect to a ₀ ,..., A ₂₃ , for example, parameters can be determined as shown in FIG.

Further, to ensure that the travel distance distribution time zone adjacent changes smoothly is the difference _{Δa t = a t -a t +} 1 is "0" in the parameters of the time adjacent zone (zero), or , the ratio _{Δa t = a t / a t} + 1 may if given a prior distribution P (Δa _t), such as close to "1". In this case, for example, as shown in the following Equation 4, a normal distribution centered on “0” (zero) can be given as a prior distribution.

  In addition, when this parametric method is adopted, the parameter of the time zone in which no data represented by the travel distance information exists is determined only by the prior distribution. For this reason, as the prior distribution, for example, there is one data number represented by the travel distance information by adopting a distribution in which the average travel distance is almost “0” (zero) and the parameter has the maximum probability. The distribution of the travel distance can be naturally selected so that the travel distance is substantially “0” (zero) in the time period during which no travel is performed. When determining the parameters in this way, for example, a forgetting factor is introduced, and the older data has a smaller weight, so that it is possible to eliminate the fact that the data that has been generated by chance remains long.

ただし、前記式５中のmはワイブル係数である。 Furthermore, in this case, for example, the probability density of the distribution in which the travel distance is approximately “0” (zero) can be expressed by a delta function. However, in the Weibull distribution expressed by the following equation 5, the scale parameter T is By setting it to a small positive number, it can behave like a delta function.

However, m in the said Formula 5 is a Weibull coefficient.

Specifically, as the prior distribution P (T) of the scale parameter T, the distributions shown in the following formulas 6 and 7 may be adopted.

  Here, FIG. 7 will be described. FIG. 7 is an approximation of the cumulative mileage distribution for each time zone by a Weibull distribution, and shows a case where a prior distribution by a gamma distribution is given to a parameter in the Weibull distribution. As is clear from FIG. 7, it can be understood that the data represented by the actual travel distance information indicated by the solid line and the approximation by the Weibull distribution indicated by the broken line are well approximated by the Weibull distribution. In particular, in a time zone in which no data represented by the travel distance information exists, the cumulative frequency suddenly approaches “1” near the travel distance of “0” (zero), and the travel distance is appropriately “0”. (Zero). In addition, 9 o'clock and 10 o'clock are cases where there are several points (one or two points) of data represented by long-distance travel distance information. ”(Zero), it can be understood that the travel distance increases rapidly in the vicinity of“ 0 ”(zero) and then gradually increases as the cumulative distribution.

  In this way, if the parametric method is adopted and the distribution f (d | t) in the mileage direction can be determined, the distribution f (d | t) is integrated in the mileage direction to obtain the starting point. A cumulative frequency distribution F (d | t) for each time can be obtained. Then, in this cumulative frequency distribution F (d | t) for each departure time, by determining a travel distance equal to any predetermined probability (hereinafter referred to as “predetermined safety probability”) for each departure time, It is possible to obtain the mileage to be secured by departure time and safety probability. Needless to say, the above-mentioned methods a to c can be combined to create the estimated travel distance map for each departure time zone for weekdays and holidays. Then, the travel distance prediction map generation unit 33 creates the predicted travel distance map for each departure time zone for each weekday and holiday by using the travel distance information and the predicted departure time information, and stores the predicted travel distance map for each departure time zone as data. This is supplied to the output unit 34.

  In the data output unit 34, the predicted departure time map and the predicted arrival time map supplied from the departure / arrival time prediction map generation unit 32, and the predicted travel distance map for each departure time zone supplied from the travel distance prediction map generation unit 33. Based on the above, various information (data) is generated and output (provided). Hereinafter, the provision of information by the data output unit 34 will be specifically described as an example.

  As described above, the departure / arrival time prediction map generation unit 32 generates a predicted departure time map and an arrival time map for each weekday and holiday, and the travel distance prediction map generation unit 33 sets the departure time zone for each weekday and holiday. By creating the predicted mileage map, it is possible to accurately predict the daily behavior pattern (travel pattern) of the vehicle and the daily behavior pattern (lifestyle) using the user's vehicle, that is, the usage status of the vehicle. it can. By being able to appropriately predict the usage status of the vehicle in this way, the present apparatus 10 can travel when the vehicle is parked at a predetermined parking position, when the vehicle is out, or when traveling outside. The distance can be grasped well. Therefore, if the vehicle is a rechargeable vehicle, the electronic control unit 11 of the present apparatus 10, more specifically, the data output unit 34, completes charging of the battery efficiently or reliably or discharges the power of the battery. Information (data) can be provided. Hereinafter, a case where information useful for charge / discharge control of the vehicle is provided will be described in detail.

  Charging / discharging control of a rechargeable vehicle is performed by electrically connecting a charging / discharging outlet of a charging / discharging stand, which is a charging / discharging facility installed in a specific parking lot such as a predetermined parking position, and a battery mounted on the vehicle. To be executed. For this reason, the charging / discharging stand is provided with a control device for controlling charging or discharging of the vehicle battery. The control device has a microcomputer as a main component, and performs short-range wireless communication with a vehicle communication means, so that various information (for example, a request code and a response code in charging) can be obtained at predetermined intervals. Vehicle ID, SOC (State Of Charge) which is a value (amount of electric power) indicating a charging state of a battery mounted on the vehicle, and the like. Thereby, the control device of the charging / discharging stand can supply and charge an appropriate amount of electric power to the battery mounted on the vehicle, or can discharge an appropriate amount of electric power from the battery.

  When the control device provided in the charge / discharge stand performs charge / discharge control on the rechargeable vehicle in this way, the electronic control unit 11 of the present device 10, more specifically, the data output unit 34, As shown in FIG. 8, with respect to the control device of the discharge stand, as shown in FIG. 8, a rule curve representing the relationship between the estimated required power amount that requires collateral by each time with respect to each time. In other words, a rule curve representing the remaining battery level (the remaining battery level and the minimum required battery level that have not been used up in the previous run) for each time (by time period) is output and provided. More specifically, in providing the rule curve to the control device of the charging / discharging stand, first, the vehicle receives the vehicle ID and the unit electric energy charged in the battery of the vehicle in the device 10 via the communication means. Mileage, that is, so-called electricity consumption information representing electricity consumption is transmitted. In this apparatus 10, the electronic control unit 11 receives the vehicle ID transmitted from the vehicle side via the communication unit 12, and searches the vehicle ID database 13a using the received vehicle ID to identify the vehicle. .

  Subsequently, in the electronic control unit 11, the data output unit 34 predicts the estimated travel distance by departure time zone according to weekdays and holidays created by the travel distance prediction map generation unit 33 corresponding to the vehicle ID of the identified vehicle. While acquiring a map, the power consumption information from the specified vehicle is acquired. Then, the data output unit 34 divides the predicted travel distance for each time zone by the power cost of the vehicle represented by the power cost information based on the obtained estimated travel distance map for each departure time zone. A rule curve is created by estimating and determining the amount of electric power (charge amount) required in step. Further, the data output unit 34 acquires a predicted departure time map and an estimated arrival time map for each weekday and holiday created by the departure / arrival time prediction map generation unit 32 corresponding to the vehicle ID of the specified vehicle.

  As described above, when the rule curve corresponding to the vehicle to be charged and discharged is created, the data output unit 34 outputs the created rule curve. When the predicted departure time map and the estimated arrival time map corresponding to the vehicle that is the target of charge / discharge control are acquired, the data output unit 34 outputs the acquired estimated departure time map and estimated arrival time map. As a result, in the present apparatus 10, the vehicle information is provided as provision information by combining the rule curve information representing the created rule curve and the time map information representing the estimated departure time map and the estimated arrival time map via the communication unit 12. To the side.

  In the vehicle, the provision information transmitted from the apparatus 10 is received via the communication means and stored in the storage means in the vehicle. In this way, by storing provision information including rule curve information representing a rule curve on the vehicle side and time map information representing an estimated departure time map and an expected arrival time map, for example, in addition to a predetermined parking position Even when the vehicle is parked and charged / discharged, the provision information stored in the charging stand is output to the installed charging stand, so that the rules provided from the device 10 are also provided at the charging stand as described later. Charge / discharge control using a curve, a predicted departure time map, and an estimated arrival time map can be executed.

ただし、前記式８の右辺θ(t)は時刻tにおけるバッテリへの充電可能量を表し、前記式８中のT(t)は時刻tにおける予想気温を表し、前記式８中のVは充電電圧を表し、前記式８中のAは充電電流を表す。従って、充放電スタンドの制御装置は、予想気温T(t)、充電電圧V及び充電電流Aを入力し、これら入力した各値のうちの少なくとも一つを用いて前記式８によって表される線分（例えば、接線）と所定のバッテリ残量との交点となる推定最遅充電開始時刻を決定する。尚、このように決定される推定最遅充電開始時刻は、ルールカーブにおける局所最大値となる時刻までに充電を完了させるときの最も遅い充電開始時刻として推定される。 And a vehicle transmits the provision information memorize | stored in the memory | storage means to the control apparatus of the charge / discharge stand electrically connected, for example via near field communication. In the control device of the charge / discharge station, the provision information provided from the device 10 via the vehicle is used to accurately create and execute a charge plan for charging the vehicle battery or a discharge plan for discharging from the vehicle battery. To do. Specifically, the control device, for example, in the rule curve represented by the rule curve information in the provided information, as shown in FIG. 9, a point near the local maximum value of the estimated required power amount that is accurately predicted. Assuming a line segment (for example, a tangent line) represented by the following expression 8 from (for example, a contact), the vehicle 10 travels by consuming the power of the battery 11 the most when it requires the most power, in other words. The estimated latest charging start time for completing charging with an appropriate amount of power by the time period is estimated and determined. The line segment represented by the following formula 9 is set as a line segment that is in contact with the rule curve in the vicinity of the local maximum value or that always has a value larger than the estimated required power amount by the rule curve.

However, the right side θ (t) of Equation 8 represents the chargeable amount of the battery at time t, T (t) in Equation 8 represents the expected temperature at time t, and V in Equation 8 represents charging. Represents the voltage, and A in Equation 8 represents the charging current. Therefore, the controller for the charging / discharging stand inputs the predicted temperature T (t), the charging voltage V, and the charging current A, and uses the line represented by Equation 8 using at least one of these input values. An estimated latest charging start time that is an intersection of a minute (for example, a tangent) and a predetermined remaining battery level is determined. Note that the estimated latest charging start time determined in this way is estimated as the latest charging start time when charging is completed by the time of the local maximum value in the rule curve.

  On the other hand, in the time zone that exceeds the local maximum value in the rule curve, the estimated required power amount required for traveling becomes small. For this reason, for example, when the vehicle battery is charged with power more than that required for traveling, that is, when surplus power is included, the surplus power is discharged as power used in a house, for example. You can also In this way, the discharge plan for discharging electric power from the battery can be accurately created based on the rule curve represented by the rule curve information by the control device of the charging station, similarly to the charging plan.

ただし、前記式９の右辺φ(t)は時刻tにおけるバッテリからの放電可能量を表す。従って、充放電スタンドの制御装置は、予想気温T(t)、充電電圧V及び充電電流Aを入力し、これら入力した各値のうちの少なくとも一つを用いて前記式９が成立する放電停止時刻（推定最遅放電停止時刻）を決定することができる。 Specifically, the control device, for example, in the rule curve represented by the rule curve information, as shown in FIG. 9, a point (for example, a contact point) near the local maximum value of the estimated required power amount that is accurately predicted. From the above, assuming a line segment (for example, tangent) represented by the following formula 9 configured similarly to the formula 8, the discharge stop time at which the discharge from the time zone in which the surplus power exists is completed is determined. To do. The line segment represented by the following formula 9 is set as a line segment that is in contact with the rule curve in the vicinity of the local maximum value or that always has a value larger than the estimated required power amount by the rule curve.

However, the right side φ (t) of Equation 9 represents the dischargeable amount from the battery at time t. Therefore, the control device of the charge / discharge station inputs the predicted temperature T (t), the charge voltage V, and the charge current A, and stops the discharge in which the equation 9 is established using at least one of these input values. The time (estimated latest discharge stop time) can be determined.

Further, in the control device of the charge / discharge station, charge / discharge control of the battery of the electrically connected vehicle is performed using the estimated departure time map and the estimated arrival time map represented by the time map information in the provided information. It is also possible. That is, in this case, the control device uses the predicted arrival time map that is accurately predicted, and the time when the vehicle that is the target of charge / discharge is likely to be parked at the predetermined parking position (that is, the appearance) The cumulative probability (safety probability) P _th is specified as a predetermined safety probability (for example, 90%). The predetermined safety probability may be set in advance or may be changed as appropriate. Thus, by specifying a time (time zone) where the vehicle is likely to be parked at a predetermined parking position, the control device supplies power to the vehicle battery after this specified time (time zone). It can be supplied and charged, or power can be discharged from the vehicle battery. Further, in a situation where charge / discharge control is being executed, the control device is highly likely to depart from a predetermined parking position for a vehicle to be charged / discharged using a predicted predicted departure time map. The time (that is, the time when the occurrence cumulative probability (safety probability) P _th is equal to or higher than a predetermined safety probability (for example, 90%)) is specified. The predetermined safety probability may be set in advance or may be changed as appropriate. Thus, by specifying the time (time zone) where the vehicle is likely to depart from the predetermined parking position, the control device supplies power to the battery of the vehicle by this specified time (time zone). Can be charged or discharged from the vehicle battery.

  In this way, the electronic control unit 11 of the present apparatus 10, more specifically, the data output unit 34 provides rule curve information representing a rule curve, time map information representing an estimated departure time map and an estimated arrival time map as provided information. Is provided to the control device of the charging station, so that the control device of the charging station can execute the charge / discharge control very accurately and reliably. As a result, a user who uses the vehicle that is the target of such charge / discharge control can reliably use the vehicle with a good battery charge state, and can enjoy very good convenience.

  Further, as described above, the departure / arrival time prediction map generation unit 32 generates a predicted departure time map and an arrival time map for each weekday and holiday, and the travel distance prediction map generation unit 33 sets the departure time for each weekday and holiday. By creating a predicted mileage map by band, the daily behavior pattern (traveling pattern) of the vehicle and the daily behavior pattern (lifestyle) using the user's vehicle, that is, the usage status of the vehicle are accurately predicted. be able to. By being able to appropriately predict the usage status of the vehicle in this way, the present apparatus 10 can travel when the vehicle is parked at a predetermined parking position, when the vehicle is out, or when traveling outside. The distance can be grasped well. For this reason, in car sharing in which a plurality of users share a vehicle, the electronic control unit 11 of the present apparatus 10, more specifically, the data output unit 34, provides information for efficiently and reliably using the vehicle to the user. (Data) can be provided. Hereinafter, a case where information useful for car sharing is provided will be described in detail.

When using car sharing, the user accesses the present apparatus 10 using an information terminal apparatus (for example, a mobile phone, a smartphone, a tablet terminal, a personal computer, etc.) that the user owns. For this access, any method such as an Internet line network, a dedicated line network, or a direct wired connection may be employed by a known method. Then, when accessing the apparatus 10, the user designates a time zone in which the user wants to use the vehicle together with a user ID registered in advance. Here, when designating the time zone in which the user wants to use the vehicle, the appearance cumulative probability (safety probability) P _th , P _hd in the time zone in which the vehicle is used is appropriately set via the information terminal device, in other words, A predetermined safety probability can be arbitrarily set. As a result, the vehicle can be used more reliably in the time zone specified by the user as the predetermined safety probability is set higher so that the user increases the appearance cumulative probability (safety probability) P _th , P _hd. It may be difficult to find a vehicle that completely satisfies the conditions specified by. On the contrary, although the user can easily find an available vehicle including the conditions specified by the user as the predetermined safety probability is set low so that the cumulative probability of occurrence (safety probability) P _th and P _hd is low, The time zone specified by the user may be limited.

As described above, when the user accesses the apparatus 10 to use car sharing, the electronic control unit 11 of the apparatus 10, more specifically, the data output unit 34 predicts the usage status of the apparatus 10. With respect to all vehicles and all users, the estimated departure time map and arrival time map for each weekday and holiday created by the departure / arrival time prediction map generation unit 32 are acquired, and the travel distance prediction map generation unit 33 generates Get the estimated mileage map by departure time zone by weekdays and holidays. And the data output part 34 analyzes the utilization condition of all the vehicles and all the users based on the predicted predicted departure time map and predicted arrival time map, and the predicted travel distance map for each departure time zone. At this time, the data output unit 34 analyzes the use situation in consideration of the magnitudes of the appearance cumulative probabilities (safety probabilities) P _th and P _hd set by the user.

  By executing such an analysis, the data output unit 34 extracts a vehicle that can be used in a time zone specified by the user, and outputs usable vehicle information representing the available vehicle as provided information. In this case, for example, the data output unit 34 has a low predetermined safety probability set by the user and the time zone specified by the user is limited (specifically, shortening the usable time or changing the use start time). Etc.), information indicating that is also output as provided information. Thereby, in this apparatus 10, it transmits to a user's information terminal device as provision information via the communication unit 12. FIG.

  When using the extracted vehicle based on the provided provision information, the user transmits reservation information for reserving the use of the vehicle to the car sharing manager. As a result, the user can reserve in advance a vehicle that can be used in the time zone set by the user in advance, and the reserved vehicle is already parked at a predetermined parking position as scheduled with high probability. Therefore, the vehicle can be used very smoothly.

  As can be understood from the above description, according to the above embodiment, the departure time and arrival time for each vehicle or each user who uses the vehicle, and the travel distance for each departure time zone are used using the travel history of the vehicle. It can be predicted with extremely high accuracy. Therefore, it is possible to predict and grasp the usage status of the vehicle with extremely high accuracy. Thus, by accurately predicting the usage status of the vehicle, for example, by the time when the user uses the vehicle in charging or discharging from the battery required when the vehicle is a rechargeable vehicle, for example. It is possible to reliably complete, and to provide (present) information on a vehicle that can be used reliably in a time zone desired by the user in car sharing in which a plurality of users share and use the vehicle.

  The implementation of the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the object of the present invention.

  For example, in the above-described embodiment, the travel distance prediction map generation unit 33 uses a nonparametric technique or a parametric method to calculate a stair-like coarse distribution obtained in a situation where the number of data is small at a constant time interval (for example, one hour interval). The method was adopted so that a predicted mileage map for each departure time with a smooth distribution was obtained. In this case, according to the number of existing data, for example, the time interval can be changed to a variable length so that the number of data is evenly included. That is, in this case, the time interval of the time period in which the number of data decreases (for example, at night) is increased to increase the number of data included in the time interval, and conversely in the time period in which the number of data increases. Shorten the time interval to reduce the number of data. In this way, by changing the time interval so that the number of data existing in each time interval is increased or decreased (or equalized), the stepwise coarse distribution is improved and smooth start time zones are separated. An expected mileage map can be obtained.

  DESCRIPTION OF SYMBOLS 10 ... Vehicle use assistance apparatus, 11 ... Electronic control unit, 12 ... Communication unit, 13 ... Memory | storage unit, 13a ... Vehicle ID database, 13b ... User ID database, 13c ... Vehicle travel history database, 31 ... Data input part, 32 ... Departure / arrival time prediction map generation unit, 33 ... travel distance prediction map generation unit, 34 ... data output unit

Claims (11)

A vehicle usage support device that includes usage status prediction means for predicting the usage status of a vehicle by a user, and that supports the usage of the vehicle by the user based on the predicted usage status of the vehicle,
The usage status prediction means includes:
Obtain information on the usage status of past vehicles for each vehicle or for each user who uses the vehicle,
Based on the acquired information, future information on departure time from a predetermined parking position and information on arrival time at the predetermined parking position are predicted for each vehicle or each user who uses the vehicle. Decide
Based on the acquired information and the information on the predicted departure time, information on the travel distance traveled by the vehicle that departed in the departure time zone including the departure time represented by the information on the predicted departure time in the future, Predicting and determining for each or every user using the vehicle,
Predicting the use status of the vehicle for each vehicle or for each user using the vehicle, using either the information about the predicted departure time, the information about the predicted arrival time, or the information about the predicted travel distance, A vehicle use support apparatus that provides information reflecting a predicted use situation of a vehicle to a user. In the vehicle use support device according to claim 1,
Information on the usage status of the past vehicle is as follows:
Departure time information representing departure time from the predetermined parking position, arrival time information representing arrival time at the predetermined parking position, and departure from the predetermined parking position at the departure time represented by the departure time information. It is one of the mileage information that represents the mileage after
Information about the predicted departure time is as follows:
Information representing the cumulative probability of appearance of the departure time represented by the departure time information,
Information on the predicted arrival time is:
Information representing the cumulative probability of arrival of the arrival time represented by the arrival time information;
Information about the predicted mileage is
The vehicle use support apparatus, characterized in that the vehicle use support apparatus is information representing an accumulated cumulative appearance probability of a travel distance represented by the travel distance information in a departure time zone including a departure time having an appearance cumulative probability of the departure time. In the vehicle use support device according to claim 2,
The usage status prediction means includes:
Consider at least the safety probability set by the user using the vehicle to determine the cumulative probability of appearance to be secured against the cumulative probability of appearance of the departure time or the cumulative probability of appearance of the arrival time. And
A vehicle usage support apparatus that predicts the usage status of a vehicle for each vehicle or for each user who uses the vehicle, and provides the user with information reflecting the predicted usage status of the vehicle. In the vehicle use support device according to claim 2 or claim 3,
Information about the predicted mileage,
The travel distance information in the departure time zone including the departure time having the cumulative probability of appearance of the departure time using at least one of statistical frequency method, non-parametric method, and parametric method A vehicle usage support apparatus, characterized in that it is calculated as the cumulative probability of occurrence of a travel distance represented by: In the vehicle use support device according to claim 4,
The statistical parametric method is based on a parameter of a statistical model representing a distribution of appearance cumulative probability of travel distance represented by the travel distance information in a departure time zone including a departure time having a start probability of appearance of the departure time. A vehicle use support apparatus using a statistical model that gives a prior distribution. In the vehicle use support device according to any one of claims 1 to 5,
Information reflecting the predicted vehicle usage status is:
A vehicle use support apparatus, which is information relating to charge / discharge control for controlling charging or discharging of a battery mounted on a vehicle. In the vehicle use support device according to claim 6,
The usage status prediction means includes:
Based on the information on the power consumption of the battery mounted on the vehicle in the past for each vehicle or for each user who uses the vehicle, the relationship of the estimated required power amount that must be secured by that time for each time Create a rule curve to represent
Using the created rule curve to estimate and determine the charging start time or discharge stop time from the battery,
A vehicle usage support apparatus, characterized in that it provides information on charge / discharge control for controlling charging or discharging of the batteries mounted on both according to the charging start time or the discharge stop time determined by the estimation. In the vehicle use support device according to claim 7,
The usage status prediction means includes:
Using the created rule curve, a line segment that is in contact with the rule curve in the vicinity of the local maximum value in the rule curve or has a value that is always larger than the estimated required electric energy by the rule curve is set, and the battery The latest charge start time that is the latest time among the charge start times to, or the latest discharge stop time that is the latest among the discharge stop time from the battery is estimated and determined,
A vehicle for providing charge / discharge control information for controlling charging or discharging of the batteries mounted on the both in accordance with the latest charging start time or the latest discharging stop time determined by the estimation. Usage support device. In the vehicle use support device according to claim 8,
The usage status prediction means includes:
The line segment for estimating and determining the latest charging start time is
Set using at least one of the chargeable amount to the battery, the expected temperature when charging the battery, the charging voltage supplied to the battery and the charging current supplied to the battery, or
The line segment for estimating and determining the latest discharge stop time,
Vehicle use support characterized in that it is set using at least one of a dischargeable amount by the battery, an estimated temperature when the battery is discharged, a discharge voltage supplied from the battery, and a discharge current supplied from the battery. apparatus. In the vehicle use support device according to any one of claims 1 to 5,
Information reflecting the predicted vehicle usage status is:
A vehicle use support apparatus, which is information related to car sharing in which a vehicle is shared by a plurality of users. In the vehicle use support device according to claim 10,
The usage status prediction means includes:
Using the information about the departure time and the information about the arrival time determined and predicted for each vehicle or for each user who uses the vehicle, a vehicle that can be used among a plurality of vehicles is extracted,
The vehicle use support apparatus characterized in that the extracted vehicle is provided as information related to the car sharing.

Images