JP2017125825A - Electric vehicle running supporting device, on-vehicle device, and running supporting method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a running supporting device, an on-vehicle device, and a supporting method that realize a function of effectively supporting running of an electric vehicle, with a predicted risk generation for each running interval taken into consideration.SOLUTION: A running supporting server 10 includes information acquisition units 102 to 106, a predicted risk calculation unit 110 and an arrival probability calculation unit 111. The information acquisition units acquire information on elements related to running, including a battery residual amount. The predicted risk calculation unit calculates the average value of the electric cost of an electric vehicle and the standard deviation of the electric cost in a running interval based on the element information and an electric cost estimation model. The arrival probability calculation unit calculates the arrival probability in the running interval based on the battery residual amount and the calculation result obtained by the predicted risk calculation unit.SELECTED DRAWING: Figure 2

Description

  Embodiments described herein relate generally to a travel support device, an in-vehicle device, and a travel support method that support travel of an electric vehicle or the like.

  In general, an electric vehicle (EV) has a relatively short travelable distance due to the limitation of battery capacity. Moreover, there are few installation places of charging facilities (charger), such as a charging station which can charge the battery for EV. In recent years, driving support servers and systems that can predict the reach of an electric vehicle based on the current battery level have been proposed.

International Publication WO2012-035847 JP 2011-232241 A JP 2006-053132 A

  In the conventional travel support server and system, the prediction risk that occurs in each travel section of the electric vehicle is not taken into account in the prediction result of the reach distance. For this reason, when the user of an electric vehicle travels according to a prediction result aiming at the place where a charging station exists, there is a possibility that the remaining battery power may be exhausted before reaching. Therefore, there is a problem of realizing an effective driving support function of an electric vehicle in consideration of a prediction risk.

  The driving support apparatus for an electric vehicle according to the present embodiment includes an information acquisition unit, a prediction risk calculation unit, and an arrival probability calculation unit. The information acquisition means acquires travel-related element information including the remaining battery level of the electric vehicle. The prediction risk calculation means calculates an average value of the electric cost of the electric vehicle and a standard deviation of the electric cost in the traveling section based on the element information and the electric cost estimation model. The arrival probability calculation means calculates an arrival probability in the travel section based on the remaining battery level and the calculation result of the prediction risk calculation means.

The block diagram for demonstrating the outline of driving assistance systems, such as an electric vehicle regarding embodiment. The block diagram for demonstrating the structure of the driving assistance server regarding 1st Embodiment. The flowchart for demonstrating operation | movement of the driving assistance server regarding 1st Embodiment. The figure for demonstrating the specific example of the log | history information regarding 1st Embodiment. The figure for demonstrating the specific example of the parameter information regarding 1st Embodiment. The flowchart for demonstrating the process of the parameter learning part regarding 1st Embodiment. The figure for demonstrating the relationship between the process of the parameter learning part regarding 1st Embodiment, and historical information. The flowchart for demonstrating the process of the prediction risk calculation part regarding 1st Embodiment. The figure for demonstrating utilization of the power consumption estimation model regarding 1st Embodiment. The flowchart for demonstrating the process of the arrival probability calculation part regarding 1st Embodiment. The figure which shows an example of the display form of the indicator regarding 1st Embodiment. The block diagram for demonstrating the structure of the driving assistance server regarding 2nd Embodiment.

Embodiments will be described below with reference to the drawings.
The electric vehicle or the like in the present embodiment is not limited to an electric vehicle that runs only on electricity, and may be any vehicle that can run on other than volatile fuels such as gasoline and light oil. For example, the present invention can be applied to a hybrid vehicle that uses a gasoline engine and a motor that uses electricity, an automobile equipped with a fuel cell using hydrogen, and the like.
FIG. 1 is a block diagram showing an outline of a driving support system 1 such as an electric vehicle according to first and second embodiments described later. As shown in FIG. 1, the driving support system 1 schematically includes a driving support server 10, a charging information server 30, and an environment information server 40. Furthermore, the driving support system is configured to provide information to the terminal 20 via the relay device 50. As will be described later, the driving support server 10 has a computer system that calculates and outputs a predicted power consumption value (predicted distance value) and a reachable distance probability of an electric vehicle or the like as a main element, and wireless communication that exchanges various types of information. It is the structure containing an apparatus. As will be described later, the charging information server 30 provides the driving support server 10 with charging information including predicted battery remaining amount information for each electric vehicle or the like.

  The environment information server 40 includes a traffic jam information server 41, a weather information server 42, and a database 43. The traffic jam information server 41 provides the travel support server 10 with traffic jam information (for example, converted into an average speed value) for each travel section. The weather information server 42 provides the travel support server 10 with weather information (particularly weather forecast information including temperature) for each travel section. The database 43 stores information such as road gradient information after the travel section and coefficients in a power consumption estimation model described later. The environment information server 40 provides the travel support server 10 with information such as road gradient information and coefficients from the database 43.

  Further, the relay device 50 exchanges information between the travel support server 10 and the terminal 20. Here, when the terminal 20 is a mobile terminal such as a mobile phone or a smartphone operated by a user inside an electric vehicle or the like, the relay device 50 is a base station of a mobile phone network or a public wireless LAN access point such as WiFi. Etc. The terminal 20 may be an in-vehicle device such as a car navigation mounted on an electric vehicle. When the terminal 20 is an in-vehicle device, the in-vehicle device is configured to be able to communicate with the relay device 50 via wireless communication such as an ITS spot. In this case, the relay device 50 is configured by roadside communication equipment such as an ITS spot.

[First embodiment]
FIG. 2 is a diagram illustrating a configuration of the travel support server 10 according to the first embodiment. Hereinafter, with reference to FIG. 2, the structure of the driving assistance server 10 of the embodiment will be described.
The travel support server 10 includes a position information acquisition unit 101, a gradient information acquisition unit 102, a weather information acquisition unit 103, a traffic jam information acquisition unit 104, a user information acquisition unit 105, a battery remaining amount information acquisition unit 106, and a history information database (DB). 107 is included. Further, the driving support server 10 includes a parameter learning unit 108, a parameter information database (DB) 109, a prediction risk calculation unit 110, and an arrival probability calculation unit 111.

  Here, the travel support server 10 is installed on a roadside such as an expressway on which an electric vehicle travels, for example, and exchanges information with the terminal 20 via the relay device 50. Hereinafter, for the sake of convenience, there is a case where the description via the relay device 50 is omitted. The position information acquisition unit 101 acquires position information (GPS position information) such as an electric vehicle that is running from the terminal 20. The environment information server 40 predicts a travel section of an electric vehicle or the like based on the position information provided from the position information acquisition unit 101 and provides the travel support server 10 with the predicted travel section. As described above, the position information acquisition unit 101 may receive position information from the in-vehicle device when the terminal 20 is the in-vehicle device.

  The gradient information acquisition unit 102, the weather information acquisition unit 103, and the traffic jam information acquisition unit 104 respectively receive road gradient information, weather information, and traffic jam from the traffic jam information server 41, the weather information server 42, and the database 43 included in the environment information server 40. Each piece of information is acquired for each travel section. The user information acquisition unit 105 acquires user information including user ID information and the like from the terminal 20. The user information is linked to, for example, driving history information (driving history information) for each user stored in the database 43 of the environment information server 40.

  The battery remaining amount information acquisition unit 106 acquires battery remaining amount information in which the remaining amount of battery (power amount that can be output) of an electric vehicle or the like is predicted from the charging information server 30. The charge information server 30 corresponds to a charge management device that provides the predicted remaining battery level information to the remaining battery level information acquisition unit 106.

  The history information DB 107 includes element information acquired by each of the position information acquisition unit 101, the gradient information acquisition unit 102, the weather information acquisition unit 103, the traffic jam information acquisition unit 104, the user information acquisition unit 105, and the battery remaining amount information acquisition unit 106. For example, one year (N days) is stored for each travel section.

As will be described later, the parameter learning unit 108 uses, for example, one year (N days worth) of element information accumulated in the history information DB 107, and uses a plurality of parameters (coefficients and standard deviations of the electricity costs) of the electricity cost estimation model. Learn and set.
The parameter information DB 109 stores parameter information set by the parameter learning unit 108. Here, the power consumption estimation model is a calculation formula for calculating power consumption (EV fuel consumption = reach distance / power consumption) from element information in the EV travel section. In other words, the predicted value of the reach distance can be calculated from the power consumption calculated by the power consumption estimation model. Therefore, the predicted value related to the reach distance includes the power cost calculated by the power cost estimation model.

  As will be described later, the prediction risk calculation unit 110 uses the parameters extracted from the parameter information DB 109 to calculate the above-described power consumption (its average value μ) and the power cost standard deviation σ. This standard deviation σ is the square root of the variance, and indicates the variation with respect to the power consumption (its average value μ). That is, the prediction risk calculation unit 110 calculates an average value and a standard deviation of power consumption for calculating a risk when the reach distance is predicted.

  The arrival probability calculation unit 111 calculates the arrival probability in the travel section based on the calculation result of the prediction risk calculation unit 110, as will be described later. The travel support server 10 transmits travel support information including the calculation result of the arrival probability calculation unit 111 to the terminal 20 via the relay device 50. The terminal 20 displays and outputs the received travel support information on the display device 100 installed. When the terminal 20 is an in-vehicle device, the display device 100 is a display device incorporated in the in-vehicle device. As will be described later, the driving support information is displayed on the display device 100 in a predetermined display form (see FIG. 11).

  Next, the operation of this embodiment will be described with reference to FIGS. FIG. 3 is a flowchart for explaining the operation of the driving support server 10.

  As shown in FIG. 3, the driving support server 10 includes EV information obtained by a gradient information acquisition unit 102, a weather information acquisition unit 103, a traffic jam information acquisition unit 104, a user information acquisition unit 105, and a battery remaining amount information acquisition unit 106. Travel-related element information (may be referred to as related information) is acquired (step S1). Here, the related information corresponds to the EV traveling section predicted based on the position information of the position information acquisition unit 101, and includes gradient information, weather information, traffic jam information, user information, and battery remaining amount information. However, the user information may not be included in the related information.

The travel support server 10 stores the acquired travel-related element information (related information) in the history information DB 107 (processing S2). In the present embodiment, in the history information DB 107, related information for N days (for example, for one year) is accumulated for every minute, for example, every travel section, and is stored as history information.
FIG. 4 is a diagram showing a specific example of history information stored in the history information DB 107 in the process S2 of FIG. As shown in FIG. 4, in the history information item, the user ID is ID information set by the user information acquisition unit 105. The position is position information acquired by the position information acquisition unit 101. The remaining charge is the remaining battery information set by the remaining battery information acquisition unit 106.

  Further, in the history information item illustrated in FIG. 4, the section and the gradient are gradient information indicating the traveling section set by the gradient information acquisition unit 102 and the gradient. Here, the gradient information acquisition unit 102 acquires the gradient information from the environment information server 40. The environment information server 40 generates gradient information based on the position information provided from the position information acquisition unit 101 with reference to, for example, the section table stored in the database 43 and the height table associated therewith. Here, the code “1” shown in the section item is associated with the travel section name (for example, XX motorway ΔΔIC to □□ IC) recorded in the section table. In addition, the slope information indicated by the slope item is expressed as an accumulated value of the increment or decrease of the reference power consumption, and indicates an uphill accumulated value as a sum of positive values and a downhill accumulated value as a sum of negative values. Here, the value “10” indicates flatness and the value “15” indicates uphill.

  Furthermore, in the history information items illustrated in FIG. 4, the temperature and the traffic jam are the weather information and the traffic jam information set by the weather information acquisition unit 103 and the traffic jam information acquisition unit 104, respectively. As shown in FIG. 1, the environment information server 40 includes a traffic jam information server 41 and a weather information server 42. The weather information acquisition unit 103 refers to the temperature table created from the weather forecast information of the weather information server 42, and sets the temperature as the weather information of the travel section. Further, the traffic jam information acquisition unit 104 refers to the traffic jam table created by the traffic jam information server 41 and sets the traffic jam (average speed value) of the travel section.

  Returning to FIG. 3, when the process S <b> 2 is completed, the parameter learning unit 108 of the driving support server 10 uses, for example, related information (element information) for one year (N days) accumulated in the history information DB 107, A parameter learning process for learning a plurality of parameters (coefficients) of the power consumption estimation model is executed (YES in process S3, process S4). The parameter information set by the parameter learning process is stored in the parameter information DB 109.

  FIG. 5 is a diagram showing a specific example of the parameter information stored in the parameter information DB 109 in the process S4 of FIG. As shown in FIG. 5, the parameter information is set for each user and each travel section, for example, by setting a plurality of coefficients (a) and one standard deviation (σ) of the linear model as a set. In addition to this, the parameter information may be set for each user or each EV vehicle type in common travel sections. Moreover, a user may be common and you may set for every driving | running | working area or common driving | running | working area. Further, the parameter information may be set for each EV vehicle type and each travel section.

Here, the parameter learning process (S4) of FIG. 3 will be described with reference to FIGS.
FIG. 6 is a flowchart for explaining an example of the process of the parameter learning unit 108 in the process 4 of FIG. FIG. 7 is a diagram for explaining the relationship between the process of the parameter learning unit 108 and the history information. Hereinafter, processing S11 to S16 of the parameter learning unit 108 illustrated in FIG. 6 will be described with reference to FIG.

  In FIG. 6, the parameter learning unit 108 extracts, from the history information shown in FIG. 7, two points with remaining charge and data 60 between them in the same user ID and travel section (processing S11). Next, the parameter learning unit 108 calculates the distance 61, the power consumption 62, the slope (gradient) 63, the temperature (average temperature) 64, and the traffic jam (average speed value) 65 based on the extracted data 60 ( Process S12). That is, the parameter learning unit 108 calculates the distance 61 that is the sum of the distances between the two points based on the position information of the two points. Further, the parameter learning unit 108 calculates the power consumption 62 as the difference between the charge remaining amount at the start and at the end. Further, the parameter learning unit 108 calculates the degree (gradient) 63 of the hill including the uphill cumulative value as the sum of the positive slope values and the downhill cumulative value as the sum of the negative slope values.

  Next, the parameter learning unit 108 calculates the power consumption 66 corresponding to the fuel consumption in the EV travel section from the calculated distance 61 and power consumption 62 according to the relational expression “power consumption = reach distance / power consumption” (processing). S13).

Further, the parameter learning unit 108 calculates a coefficient β from the calculated slope grade 63, temperature 64, traffic jam 65, and power consumption 66 from the following equation (1) (processing S14).
Here, Xi is data in which values of slope (uphill accumulated value and downhill accumulated value), temperature, and traffic jam are arranged. yi is data indicating the electricity consumption.

Further, the parameter learning unit 108 calculates the calculated standard deviation σ of the power consumption 66 from the following equation (2) (processing S15).

  The parameter learning unit 108 updates (learns) the parameter information shown in FIG. 5 based on the user ID and the travel section information, using the calculated coefficient β and the standard deviation σ of the power consumption as parameters (processing S16). In addition, said Formula (1), (2) shows the calculation formula at the time of utilizing a linear model. The parameter learning unit 108 of the present embodiment is not limited to this, and can learn parameter information in the same manner even when a method such as support vector regression or Lasso is used.

  Returning to FIG. 3 again, in the driving support server 10, after the parameter learning unit 108 learns (process S4) and saves the parameter information in the parameter information DB 109, the prediction risk calculation 110 executes the prediction risk calculation process (process). S5). The parameter learning unit 108 does not execute the parameter learning process when related information for one year (N days) is not accumulated in the history information DB 107, for example. Even in this case, the prediction risk calculation unit 110 executes the prediction risk calculation process based on the related information stored in the history information DB 107 (NO in process S3). That is, as described above, the prediction risk calculation unit 110 calculates a parameter composed of the coefficient β and the standard deviation σ of the power consumption by the same processing as the parameter learning unit 108.

Next, the flowchart of FIG. 8 shows the prediction risk calculation process of the prediction risk calculation unit 110 in the process S5 of FIG.
As illustrated in FIG. 8, the prediction risk calculation unit 110 extracts appropriate parameters from the parameter information DB 109 based on the user ID and the travel section information (processing S21). Here, as described above, the parameters are composed of the coefficient β calculated by the parameter learning unit 108 and the standard deviation σ of the electricity consumption.

Next, the prediction risk calculation unit 110 uses the coefficient β of the parameter, and based on the slope information (63), the temperature (64), and the history information of the traffic jam (65) (see FIG. 6), the power consumption y Is calculated from the following equation (3) (processing S22).
Here, X is data in which values of slope (uphill cumulative value and downhill cumulative value), temperature, and traffic jam are arranged. y is data indicating the electricity consumption. Note that the current temperature information or weather forecast information is used for each value of temperature and traffic jam. The degree of slope (cumulative uphill value and cumulative downhill value) is calculated by the same process as the parameter learning unit 108 based on past information in the travel section from the current position to the destination from the history information. For a plurality of information, the average value is used.

  The prediction risk calculation unit 110 outputs the calculated average value μ of the power consumption y and the standard deviation σ of the power consumption included in the parameter as a calculation result (processing S23). Here, the standard deviation σ of the power consumption is the square root of the variance, and shows the variation with respect to the average value μ of the power consumption. That is, the prediction risk calculation unit 110 calculates a prediction value related to the reach distance as the average value μ of the power consumption, and calculates a standard deviation σ with respect to the average value μ of the power consumption. The prediction risk calculation unit 110 repeats the above processes S21 to S23 until all the travel sections are completed (process S24).

Here, FIG. 9 is a diagram for explaining the relationship between the learning process S4 of the parameter learning unit 108 and the prediction risk calculation process S5 of the prediction risk calculation unit 110 in FIG. 3 from the viewpoint of using the power consumption estimation model. .
That is, in the learning step 90 corresponding to the learning process S4, the coefficient 901 included in the parameters of the power consumption estimation model is estimated by the power consumption calculation process 900 using various kinds of information as described above. Here, the various types of information can be classified into user individual information 902, dynamic information 903, fixed information 904, travel element information 905, EV characteristic information 906, and the like. In addition, in the execution step 91 corresponding to the prediction risk calculation process S5, the power consumption calculation using the user individual information 902, the dynamic information 903, the fixed information 904, and the EV characteristic information 906 and using the estimated coefficient 901 as input. Through the process 910, the predicted value 911 of the power consumption estimation model is output. The predicted value 911 is an average value of the power consumption based on the electric power consumption and the reach distance of the EV and a standard deviation of the power consumption related to the risk of prediction.

  Returning to FIG. 3, when the prediction risk calculation process (S5) ends, the arrival probability calculation unit 111 performs an arrival probability calculation process for calculating the arrival probability of the EV in the travel section based on the calculation result of the prediction risk calculation unit 110. Execute (Process S6).

Here, FIG. 10 is a flowchart showing the arrival probability calculation process of the arrival probability calculation unit 111 in the process S6 of FIG.
As shown in FIG. 10, the arrival probability calculation unit 111 calculates the following relational expression from the travel section i, the power consumption (average value μi), the standard deviation σi of the power consumption, the distance di of the travel section, and the current battery remaining amount S. The value (statistical value t) of the t distribution (or student distribution) is calculated by (4) (processing S31).

Next, the arrival probability calculation unit 111 calculates the lower probability (the area of the left region of the t distribution) from the calculated statistical value t of the t distribution based on a predetermined calculation formula for the t distribution (processing S32). The arrival probability calculation unit 111 outputs the calculated lower probability as the arrival probability p (processing S33).
Returning to FIG. 3 again, when the arrival probability calculation process (process S6) ends, the driving support server 10 displays and outputs the driving support information including the calculation result of the arrival probability calculation unit 111 on the display device 100 (process S7). . The display device 100 is, for example, a display device of a user's mobile terminal, or may be a display device included in an in-vehicle device such as a car navigation system. The driving support server 10 displays and outputs driving support information including not only the calculation result of the arrival probability but also a predicted value (including power consumption) regarding the remaining battery level and the reaching distance.
The driving support server 10 repeats the processes S1 to S7 until the process is completed based on the EV travel time or a request from the user (process S8).

In the process S7 of FIG. 3, FIG. 11 is a diagram illustrating an example of a display form of the display device 100 by the display output process of the driving support server 10.
As shown in FIG. 11, the display screen of the display device 100 includes, for example, a display area 200 that displays a road map of an EV travel section, a display area 210 that displays information related to predicted values on the road map, and a road A display area 211 for displaying information on the usage status of the charging station on the map is included.

Here, the road image 201 of the travel section is displayed in the display area 200. In the road image 201, for example, the minimum value, the average value, and the predicted risk of the travelable distance take into account “variance + 1” based on the predicted value in consideration of the risk calculated by the travel support server 10 in the travel section. The travel sections 202-205 corresponding to the distance and the maximum value are displayed separately in blue, yellow, orange, and red. In addition, the road image 201 displays marks 206, 207, and 208 related to the position and usage status of the charging station. The meaning of each of these marks is indicated by information displayed in the display area 211.
In addition, the display form of FIG. 11 is an example. Various display forms are possible based on the driving support information including the arrival probability calculated by the driving support server 10, the battery remaining amount, and the predicted value related to the reaching distance.

  As described above, according to the travel support server 10 of the first embodiment, when predicting the reach distance of an electric vehicle or the like based on the current remaining battery level and the power consumption estimation model in the predicted travel section. The arrival probability for taking into account the prediction risk is calculated for each travel section and each user. The travel support server 10 can display the calculated arrival probability as travel support information on the user's portable terminal or the display of the in-vehicle device 20. That is, as described above, the travel support server 10 determines the reach distance in the travel section based on the static information on the road gradient, the dynamic information such as the temperature that affects the traffic congestion and the remaining battery level. The arrival probability is calculated as prediction information considering the prediction risk that the predicted value of fluctuates. In particular, by using dynamic information, it is possible to calculate the arrival probability considering the current situation.

  Therefore, since the EV user can confirm the arrival probability for the prediction of the arrival distance based on the current battery remaining amount from the display unit, there is a high possibility that the risk that the planned destination cannot be reached can be avoided in advance. . That is, when the arrival probability is relatively low, it can be confirmed that the current remaining battery level may not reach the planned charging station location. In other words, the driving support server 10 can give the user a reference for determining where the battery can be charged based on the arrival probability for each driving section. As a result, the user can take measures to avoid a situation where the remaining battery level is exhausted before charging.

  In addition, although the said embodiment demonstrated the case where the driving assistance server 10 was installed in roadsides, such as a highway where EV drive | works, for example, it is not limited to this. Specifically, the embodiment can be applied even when a driving support device corresponding to the driving support server 10 is incorporated in an in-vehicle device such as a car navigation mounted on the EV. In this case, the driving support device may be configured to exchange information by connecting to the charging information server 30 and the environment information server 40 via wireless communication such as an ITS spot.

[Second Embodiment]
FIG. 12 is a diagram illustrating a configuration of the travel support server 10 according to the second embodiment. Hereinafter, with reference to FIG. 12, the structure of the driving support server 10 of the embodiment will be described. In addition, about the structure similar to the driving assistance server 10 regarding 1st Embodiment mentioned above, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  As illustrated in FIG. 12, the travel support server 10 of this embodiment includes a speed information acquisition unit 112, a break time calculation unit 113, and an average speed correction unit 114. Here, as in the first embodiment, the travel support server 10 of the present embodiment is installed on a roadside such as an expressway on which an EV travels, for example. The speed information acquisition unit 112 acquires speed information obtained by measuring the speed of the vehicle traveling on the road from the environment information server 40. Specifically, for example, the speed information acquisition unit 112 acquires speed information managed by the traffic information server 41 included in the environment information server 40 and measured by a traffic counter for each travel section.

  Based on the average speed information indicating the average of the speed information output from the speed information acquisition unit 112 and the average speed calculated from the travel distance of the EV, the break time calculation unit 113 is charged by the user of the EV. A break time (break time information described later) is calculated and stored in the history information DB 107. The average speed correction unit 114 subtracts the rest time from the time when the average speed is calculated, recalculates the average speed, and corrects the average speed.

Hereinafter, operations of the speed information acquisition unit 112, the break time calculation unit 113, and the average speed correction unit 114, which are main parts of the embodiment, will be described in detail.
The speed information acquisition unit 112 acquires speed information obtained by measuring the speed of the vehicle traveling in the travel section from the environment information server 40, calculates an average thereof, and outputs the average speed information. Here, the environment information server 40 estimates the travel distance of the travel section between the travel points of the EV based on the position information provided from the position information acquisition unit 101, and calculates the speed information. The speed information acquisition unit 112 stores the calculated average speed information in the history information DB 107.

Next, the break time calculation unit 113 calculates the average speed of the EV based on information from the history information DB 107 and stores it in the history information DB 107. Further, the break time calculation unit 113 calculates a difference between the average speed and the average speed information output from the speed information acquisition unit 112. The break time calculation unit 113 outputs break time information that regards the calculation result as the break time of the EV user when the EV is charged, and stores the break time information in the history information DB 107.
Here, the break time calculation unit 113 includes the previous charge end time and the current charge start time included in the remaining battery information acquired from the remaining battery information acquisition unit 106 as information from the history information DB 107. The difference and the travel distance of the EV. That is, the break time calculation unit 113 calculates an average speed from the difference between the charge end time and the charge start time and the EV travel distance, and stores the average speed in the history information DB 107.

  The average speed correction unit 114 acquires the time when the average speed of the EV is calculated from the history information DB 107 (difference between the charging end time and the charging start time), and is calculated by the rest time calculation unit 113 from the time. The average speed is recalculated by subtracting the time indicated by the break time information. The average speed correction unit 114 corrects the average speed stored in the history information DB 107 using the recalculated result.

  As described above, according to the second embodiment, the average EV speed in the traveling section can be corrected in consideration of the user's rest time during EV charging. Therefore, it is possible to improve the accuracy of the prediction process for predicting the electric power consumption of EV by using various types of information including the average EV speed stored in the history information DB 107.

  Furthermore, according to the embodiment, the break time information calculated by the break time calculation unit 113 is stored in the history information DB 107. The break time information is time information that is regarded as a break time of the EV user when the EV is charged. Here, for example, in a service area such as an expressway (rest area where EV charging facilities are installed), the break time information can be handled as information indicating the degree of congestion of the service area. Specifically, in the same service area, a plurality of users can overlap the break times in the time zone to show the distribution of the break time in the same time zone. From this distribution, it is possible to determine, as the degree of congestion, a situation where a plurality of users are resting in the same time zone.

  As described above, the break time information according to the embodiment is applied to, for example, information indicating the degree of congestion in the service area, and is used, for example, as marketing information for sales prediction such as sales of articles in the service area. A useful effect is obtained. Here, as a specific example used as marketing information, in general, it is possible to obtain a prediction result that sales such as article sales in the service area increase as the degree of congestion increases. On the other hand, it can be used as reference information for analyzing the factor of the result that sales do not increase even though the degree of congestion is high, and to examine factors that increase sales other than the degree of congestion. Naturally, various usage forms other than these are conceivable.

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

DESCRIPTION OF SYMBOLS 1 ... Driving support system, 10 ... Driving support server, 20 ... Terminal, 30 ... Charging information server,
40 ... Environmental information server, 41 ... Traffic jam information server, 42 ... Weather information server,
50 ... Relay device, 100 ... Display device, 101 ... Position information acquisition unit,
102 ... Gradient information acquisition unit, 103 ... Weather information acquisition unit, 104 ... Traffic jam information acquisition unit,
105 ... user information acquisition unit, 106 ... battery remaining amount information acquisition unit,
107 ... history information database (DB), 108 ... parameter learning unit,
109 ... Parameter information database (DB), 110 ... Predictive risk calculator,
111 ... Arrival probability calculation unit, 112 ... Speed information acquisition unit, 113 ... Break time calculation unit,
114: Average speed correction unit.

Claims (18)

Information acquisition means for acquiring travel-related element information including the battery level of the electric vehicle;
Based on the element information and the power consumption estimation model, a prediction risk calculation means for calculating the average value of the power consumption of the electric vehicle and the standard deviation of the power consumption in the traveling section;
A travel support device for an electric vehicle, comprising: an arrival probability calculation unit that calculates an arrival probability in the travel section based on the remaining battery level and the calculation result of the prediction risk calculation unit.   The driving support apparatus for an electric vehicle according to claim 1, further comprising: a parameter learning unit that learns a parameter of the power consumption estimation model based on the accumulation result of the element information.   The electric vehicle driving support apparatus according to claim 2, wherein the parameter learning unit learns the parameter including a coefficient for calculating the power consumption and a standard deviation of the power consumption. The predicted risk calculation means includes:
The driving support device for an electric vehicle according to claim 1, wherein an average value of power costs and a standard deviation of power costs are calculated based on a coefficient and a standard deviation included in parameters of the power cost estimation model. The arrival probability calculation means includes:
The arrival probability in the travel section is calculated based on the average value of the power consumption and the standard deviation of the power consumption, the distance of the travel section, and the current remaining battery level included in the calculation result of the prediction risk calculation means. A driving support device for an electric vehicle according to claim 1.   The travel support device for an electric vehicle according to claim 5, wherein the arrival probability calculation means calculates a lower probability in the t distribution as the arrival probability.   The driving support apparatus for an electric vehicle according to claim 1, further comprising display means for displaying the driving support information including the arrival probability on a display.   An in-vehicle device in which the driving support device for an electric vehicle according to claim 1 is mounted on the electric vehicle.   The in-vehicle device according to claim 8, wherein the driving support device includes a parameter learning unit that learns a parameter of a power consumption estimation model based on the accumulation result of the element information. Information acquisition means for acquiring travel-related element information including the battery level of the electric vehicle;
Speed information acquisition means for acquiring speed information obtained by measuring the speed of the vehicle traveling on the road;
A driving support device for an electric vehicle, comprising: a break time calculating means for calculating a break time from an average speed measured from a distance between charging points and time based on the element information and the speed information. The driving support device for an electric vehicle according to claim 10, further comprising: an average speed correction unit that corrects the average speed based on the break time calculated by the break time calculation unit. An electric vehicle driving support method,
A process of acquiring travel-related element information including the remaining battery level of the electric vehicle;
Based on the element information and the power consumption estimation model, a process of calculating the average value of the power consumption of the electric vehicle and the standard deviation of the power consumption in the traveling section;
A driving support method comprising: a process of calculating an arrival probability in the driving section based on a calculation result of the battery remaining amount, an average value of the power consumption, and a standard deviation of the power consumption.   The driving support method according to claim 12, further comprising a process of learning a parameter of a power consumption estimation model based on the accumulation result of the element information.   The driving support method according to claim 13, wherein the learning process learns the parameter including a coefficient for calculating the power consumption and a standard deviation of the power consumption. The process of calculating the average value of power consumption and the standard deviation of power consumption is as follows:
The driving support method according to claim 12, wherein an average value of power costs and a standard deviation of power costs are calculated based on a coefficient and a standard deviation included in parameters of the power cost estimation model. The process of calculating the arrival probability includes
The driving support method according to claim 12, wherein an arrival probability in the travel section is calculated based on the average value of the power consumption, the standard deviation of the power consumption, the distance of the travel section, and the current remaining battery level.   The driving support method according to claim 16, wherein the process of calculating the arrival probability calculates a lower probability in the t distribution as the arrival probability.   The driving support method according to claim 12, further comprising a process of displaying the driving support information including the arrival probability on a display.