JP7424186B2 - Vehicle computing device, program - Google Patents

Description

The present disclosure relates to a calculation device and a program that calculate the cruising distance of a vehicle.

Conventionally, there is a computing device for a vehicle described in Patent Document 1 below. This calculation device calculates the amount of energy consumed per unit time in a predetermined section in which the vehicle travels, based on vehicle information and road information. Further, the calculation unit estimates the energy consumption amount of the vehicle in the predetermined section based on the calculated energy consumption amount per unit time and the travel time required when the vehicle traveled in the predetermined section in the past. Further, this calculation device calculates the possible cruising distance of the vehicle from the remaining energy amount acquired from the vehicle and the estimated value of the energy consumption amount of the vehicle in a predetermined section.

International Publication No. 2012/035847

By the way, when calculating the possible cruising distance like the calculation device described in Patent Document 1, information such as vehicle information, road information, and past driving history of the vehicle is required. In this regard, for example, in a case where a vehicle uses a communication device to obtain road information from a server device or the like, if the communication device malfunctions, the calculation unit of the vehicle may not be able to obtain road information. In such a case, the calculation device cannot calculate the possible cruising distance. As described above, in the calculation device described in Patent Document 1, if any of the vehicle information, road information, and past travel history of the vehicle is lost for some reason, the possible cruising distance cannot be calculated. there is a possibility.

The present disclosure has been made in view of these circumstances, and its purpose is to provide a vehicle calculation device and program that can ensure redundancy in calculation of cruising range.

The vehicle computing device that solves the above problem is a computing device (31) that computes the cruising distance of the vehicle, and includes a route information acquisition unit (311) that acquires information about the vehicle's travel route, and a route information acquisition unit (311) that acquires information about the vehicle's travel route. A remaining energy amount acquisition unit (312) that obtains information regarding the remaining amount of energy that can be used, and a predetermined amount necessary to calculate the cruising distance separately from the information regarding the driving route and the information regarding the remaining amount of energy. The vehicle includes a parameter acquisition unit (310) that acquires the parameters of the vehicle, and a calculation unit (313) that calculates the possible cruising distance of the vehicle based on information regarding the driving route, information regarding the remaining amount of energy, and predetermined parameters. The parameter acquisition unit acquires the predetermined parameter using a second acquisition means different from the first acquisition means when the predetermined parameter cannot be acquired by the first acquisition means , and uses the second acquisition means to obtain information detectable in the own vehicle. When the predetermined parameters cannot be calculated from the own vehicle detection information as the second acquisition means, the predetermined parameters are set based on the information acquired from the other vehicle. Set.
A program for solving the above problems causes at least one processing unit (31) to acquire information regarding the traveling route of the vehicle, acquire information regarding the remaining amount of energy that can be used for traveling the vehicle, and information on the vehicle, and predetermined parameters necessary to calculate the cruising range of the vehicle separately from the information on the remaining amount of energy, and based on the information on the driving route, the information on the remaining amount of energy, and the predetermined parameters. If a predetermined parameter cannot be acquired by the first acquisition means, the predetermined parameter is acquired by a second acquisition means different from the first acquisition means, and as the second acquisition means, the own vehicle When the predetermined parameters cannot be calculated from the own vehicle detection information, the second acquisition means sets the predetermined parameters based on the own vehicle detection information, which is information that can be detected in the second acquisition means. to set predetermined parameters.

According to this configuration, even if the first acquisition means cannot acquire the predetermined parameter, the second acquisition means can acquire the predetermined parameter, so redundancy can be ensured in calculating the possible cruising distance. .
Note that the above-mentioned means and the reference numerals in parentheses described in the claims are examples showing correspondences with specific means described in the embodiments to be described later.

According to the vehicle calculation device and program of the present disclosure, redundancy can be ensured regarding the calculation of the cruising distance.

FIG. 1 is a block diagram showing a schematic configuration of a vehicle according to an embodiment. FIG. 2 is a flowchart showing the procedure of processing executed by the predictive ECU of the embodiment. FIG. 3 is a flowchart showing the procedure of vehicle parameter acquisition processing executed by the predictive ECU of the embodiment. FIG. 4 is a flowchart showing the procedure of environmental parameter acquisition processing executed by the predictive ECU of the embodiment. FIG. 5 is a map showing the relationship between outside temperature, air conditioning power, and weather, which is used by the predictive ECU of the embodiment. FIG. 6 is a flowchart showing the procedure of map information acquisition processing used by the predictive ECU of the embodiment. FIG. 7 is a graph showing a method of setting a vehicle speed profile by the predictive ECU of the embodiment. FIG. 8 is a graph showing a method of calculating the limit reaching point and possible cruising distance by the predictive ECU of the embodiment. FIG. 9 is a diagram schematically showing map information displayed on the HMI device of the embodiment.

Hereinafter, one embodiment of a vehicle computing device will be described with reference to the drawings. In order to facilitate understanding of the description, the same components are denoted by the same reference numerals as much as possible in each drawing, and redundant description will be omitted.
First, a schematic configuration of a vehicle in which the arithmetic device of this embodiment is mounted will be described. As shown in FIG. 1, the vehicle 10 of this embodiment includes a motor generator 20, an inverter device 21, a battery 22, and a clutch 23. The vehicle 10 of this embodiment is a so-called electric vehicle that uses a motor generator 20 as a driving power source.

The battery 22 consists of a secondary battery such as a lithium ion battery that can be charged and discharged. Inverter device 21 converts DC power charged in battery 22 into AC power, and supplies the converted AC power to motor generator 20 . The motor generator 20 is driven based on AC power supplied from the inverter device 21 to rotate the first power transmission shaft 24 . The first power transmission shaft 24 is connected to the second power transmission shaft 25 via the clutch 23. The clutch 23 can be switched between a connected state in which the first power transmission shaft 24 and the second power transmission shaft 25 are connected, and a non-connected state in which they are disconnected. When the clutch 23 is in the connected state, power can be transmitted between the first power transmission shaft 24 and the second power transmission shaft 25. At this time, for example, the power transmitted from the motor generator 20 to the first power transmission shaft 24 is transmitted to the wheels 28 via the second power transmission shaft 25, the differential gear 26, and the drive shaft 27, thereby causing the wheels 28 to rotate. Then, the vehicle 10 runs. When the clutch 23 is in a disengaged state, power transmission between the first power transmission shaft 24 and the second power transmission shaft 25 is interrupted.

The motor generator 20 performs regenerative power generation when the vehicle 10 is braked. Specifically, the braking force acting on the wheels 28 when braking the vehicle 10 is transmitted to the motor generator 20 via the drive shaft 27 , the differential gear 26 , the second power transmission shaft 25 , the clutch 23 , and the first power transmission shaft 24 . is input. Motor generator 20 generates electricity based on the power reversely input from wheels 28 . The electric power generated by the motor generator 20 is converted from AC power to DC power by the inverter device 21 and charged into the battery 22 .

The vehicle 10 includes an EV (Electric Vehicle) ECU (Electronic Control Unit) 30, a prediction ECU 31, and an HMI (Human Machine Interface) ECU 32. Each of the ECUs 30 to 32 is mainly composed of a microcomputer having a CPU, ROM, RAM, etc., and executes various controls by executing programs stored in advance in the ROM.

EVECU 30 is a part that controls motor generator 20. An output signal from an on-vehicle sensor 40 is input to the EVECU 30. The vehicle-mounted sensor 40 is a general term for various sensors mounted on the vehicle 10 to detect various state quantities of the vehicle 10. The on-vehicle sensor 40 includes, for example, a vehicle speed sensor that detects the traveling speed of the vehicle 10, an acceleration sensor that detects the acceleration of the vehicle 10, an accelerator position sensor that detects the depression position of the accelerator pedal, and the like. The EVECU 30 calculates a torque command value, which is a target value of torque to be output from the motor generator 20, based on vehicle state quantities such as the vehicle speed and the amount of depression of the accelerator pedal detected by the on-vehicle sensor 40, and also calculates the torque command value that is the target value of the torque to be output from the motor generator 20. Based on the torque command value, a control value for the amount of current to be supplied from the battery 22 to the motor generator 20 is calculated. EVECU 30 controls inverter device 21 based on the calculated energization control value. As a result, electric power corresponding to the energization control value is supplied from the battery 22 to the motor generator 20, so that the motor generator 20 outputs torque according to the torque command value. Furthermore, when the vehicle 10 is decelerating, the EVECU 30 controls the inverter device 21 so that the battery 22 is charged with the electric power generated by the regenerative operation of the motor generator 20 .

The EVECU 30 can obtain information about the SOC (State of Charge) value from the battery 22. Note that the SOC value defines the fully discharged state of the battery 22 as "0 [%]", the fully charged state of the battery 22 as "100 [%]", and the charged state of the battery 22 as "0 [%]". [%]" to "100[%]". The EVECU 30 transmits information on the SOC value of the battery 22 to the prediction ECU 31 in response to a request from the prediction ECU 31 . In addition, the EVECU 30 also transmits information regarding the energy efficiency η_d of the drive system, the driving force of the motor generator 20, and the efficiency of the motor generator 20 to the prediction ECU 31 in response to a request from the prediction ECU 31. The energy efficiency η_d of the drive system indicates the energy transmission efficiency when the energy of the battery 22 is transmitted to the wheels 28 via the power transmission system such as the inverter device 21, the motor generator 20, the power transmission shafts 24 and 25, etc. It is something. The information regarding the efficiency of motor generator 20 may be a constant value, or may be expressed by a map using the rotational speed and torque of motor generator 20 as variables. Further, the information regarding the efficiency of the battery 22 may be a constant value or may be the internal resistance of the battery 22.

The prediction ECU 31 is a part that calculates the possible cruising distance of the vehicle 10. The prediction ECU 31 includes a parameter acquisition section 310, a route information acquisition section 311, a remaining energy amount acquisition section 312, and a calculation section 313. In this embodiment, the prediction ECU 31 corresponds to a calculation device.
The parameter acquisition unit 310 acquires various vehicle state quantities of the vehicle 10, such as traveling speed and acceleration, based on the output signal of the on-vehicle sensor 40.

Further, the parameter acquisition unit 310 is capable of wirelessly communicating with other vehicles 60 traveling around the vehicle 10, the traffic management system 61, and the server device 62 via the communication unit 41 of the vehicle 10. The parameter acquisition unit 310 acquires various information from other vehicles 60, the traffic management system 61, the server device 62, etc. via the communication unit 41. The information that can be obtained from the other vehicle 60 includes information such as the traveling speed of the other vehicle 60 and the driving force of the power source. Information that can be obtained from the traffic management system 61 includes information on traffic congestion around the vehicle 10 and the like. The information that can be obtained from the server device 62 includes the average vehicle speed of other vehicles traveling at a predetermined point, weather information for the predetermined point, and the like.

Further, the server device 62 stores information about the past travel history of various vehicles measured in each vehicle in a database by communicating with a plurality of vehicles. The information regarding the past driving history of a plurality of vehicles stored in a database in the server device 62 includes the running resistance of the vehicle, the driving force, the running speed of the vehicle, etc. at the point where the vehicle is running. . The parameter acquisition unit 310 can acquire information regarding the past driving history of a plurality of vehicles stored in this database from the server device 62 via the communication unit 41.

Further, the parameter acquisition unit 310 can also read various information stored in the storage device 42 of the vehicle 10. The storage device 42 stores information such as the weight of the vehicle 10 and fixed values of running resistance.
Further, the parameter acquisition unit 310 acquires information regarding the energy efficiency η_d of the drive system, the energy efficiency of the battery 22, and the driving force of the motor generator 20 from the EVECU 30. Further, the parameter acquisition unit 310 also acquires various information from other ECUs installed in the vehicle 10. The parameter acquisition unit 310 can also acquire information such as air conditioning power, which is the output of the air conditioner, and energy efficiency η_a of the air conditioner, from an air conditioning ECU that controls the air conditioner, for example.

The route information acquisition unit 311 can also acquire map information stored in the storage device 42. The map information that can be obtained from the storage device 42 includes information such as the slope and curvature of the road. The map information stored in the storage device 42 includes information on three-dimensional road shapes, vehicle speed limits on the route, and road congestion conditions that can be obtained by VICS (Vehicle Information and Communication System: registered trademark). include.

The remaining energy amount acquisition unit 312 acquires the current SOC value of the battery 22 from the EVECU 30. In this embodiment, the current SOC value of the battery 22 corresponds to information on the remaining amount of energy that can be used for driving the vehicle 10.
The calculation unit 313 creates a vehicle speed profile that is a transition in the future traveling speed of the vehicle 10 based on information that can be acquired by the parameter acquisition unit 310 and information that can be acquired by the route information acquisition unit 311. Furthermore, the calculation unit 313 creates a power consumption profile, which is a change in power consumption of the battery 22, from the vehicle speed profile. Then, based on the current SOC value of the battery 22 and the power consumption profile of the battery 22 acquired by the remaining energy amount acquisition unit 312, the calculation unit 313 determines whether the vehicle will be 10 calculates the possible cruising distance D. The calculation unit 313 calculates a point reachable by the cruising distance D from the current location of the vehicle 10, and transmits information on the calculated reachable point to the HMIECU 32.

The HMIECU 32 uses the HMI device 50 to notify the occupants of the vehicle 10 of the reachable points transmitted from the prediction ECU 31 . The HMI device 50 of this embodiment is a display device that can display map information and the like. The HMIECU 32 converts the reachable point information transmitted from the prediction ECU 31 into image information etc. that can be displayed on the HMI device 50, and displays the converted reachable point image information on the HMI device 50. Thereby, the occupant of the vehicle 10 can grasp the area in which the vehicle 10 can travel by checking the reachable points displayed on the HMI device 50.

Next, the processing procedure for calculating and displaying the cruising distance executed by the predictive ECU 31 and the HMIECU 32 will be specifically described.
The route information acquisition unit 311 first extracts one or more predicted routes on which the vehicle 10 is likely to travel in the future. For example, if a destination is set in the navigation device of the vehicle 10, the route information acquisition unit 311 obtains a travel route from the current location of the vehicle 10 to the destination as a predicted route that the vehicle 10 may travel. At the same time, one or more of the travel routes are extracted. Alternatively, if the route information acquisition unit 311 has learned the travel route of the vehicle 10, the route information acquisition unit 311 uses the learned travel route as a possible route for the vehicle 10. , one or more predicted travel routes may be extracted. In this embodiment, the predicted travel route of the vehicle 10 extracted by the route information acquisition unit 311 in this manner corresponds to information regarding the travel route of the vehicle 10.

After the route information acquisition unit 311 extracts one or more predicted routes on which the vehicle 10 is likely to travel as described above, the predictive ECU 31 extracts each of the extracted single predicted route or the plurality of predicted routes. The process shown in FIG. 2 is executed for the target.
As shown in FIG. 2, the parameter acquisition unit 310 first executes a vehicle parameter acquisition process as the process of step S10. The specific procedure of this process is as shown in FIG.

When the parameter acquisition unit 310 starts the vehicle parameter acquisition process shown in FIG. 3, first, as a process in step S100, there is information on the weight and running resistance of the vehicle 10 that can be used to calculate the cruising distance D. Determine whether or not. For example, when the owner of the vehicle 10 can input unique information of the vehicle 10 such as vehicle weight and running resistance by operating the operation panel, such input information is stored in the storage device 42. In this case, the parameter acquisition unit 310 makes an affirmative determination in the process of step S100 based on the fact that the vehicle weight input value ma and the running resistance input value Fra are stored in the storage device 42. When the parameter acquisition unit 310 makes a positive determination in the process of step S100, the parameter acquisition unit 310 sets the vehicle weight input value ma and running resistance input value Fra stored in the storage device 42 in the subsequent process of step S101. After substituting the value m and the running resistance setting value Fr, the process shown in FIG. 3 ends.

If the input information of vehicle weight and running resistance is not stored in the storage device 42, the parameter acquisition unit 310 makes a negative determination in the process of step S100. In this case, the parameter acquisition unit 310 determines whether it is possible to estimate the vehicle weight and running resistance as processing in step S102. The parameter acquisition unit 310 estimates the vehicle weight and running resistance from the running speed of the vehicle 10, the driving force of the motor generator 20, etc., based on a model using a Kalman filter, for example. Note that the model using the Kalman filter may be created in advance through experiments or the like, or may be created by the parameter acquisition unit 310 through learning or the like. When calculating the respective estimated values of vehicle weight and running resistance using a Kalman filter, the parameter acquisition unit 310 calculates their error variance values. The parameter acquisition unit 310 determines that the vehicle weight and running resistance can be estimated when the error variance values of the estimated vehicle weight and running resistance are less than a predetermined threshold, and An affirmative determination is made in the process of step S102. In this case, as the process of step S103, the parameter acquisition unit 310 substitutes the estimated vehicle weight mb and the estimated running resistance value Frb, which are calculated from the model using the Kalman filter, into the vehicle weight set value m and the estimated running resistance value Frb, respectively. After that, the process shown in FIG. 3 ends.

If the error variance values of the estimated vehicle weight and the estimated running resistance are greater than or equal to a predetermined threshold, the parameter acquisition unit 310 determines that it is difficult to estimate the vehicle weight and running resistance, A negative determination is made in the process of step S101. In this case, the parameter acquisition unit 310 substitutes the vehicle weight fixed value mc stored in the storage device 42 to the vehicle weight set value m as the process of step S104. The vehicle weight fixed value mc is a value stored in advance in the storage device 42 as a value of the weight of the vehicle 10.

As a process in step S105 following step S104, the parameter acquisition unit 310 determines whether information on the running resistance of the vehicle 10 can be acquired from information regarding the past running history of other vehicles stored in a database in the server device 62. to judge.
The server device 62 acquires information regarding the past travel history from various vehicles, thereby storing the information regarding the travel history of each of the plurality of vehicles in a database. The server device 62 stores, for example, information on running resistance of each of a plurality of vehicles in a database. The running resistance information is stored in the server device 62 as, for example, frequency distribution information. The server device 62 of this embodiment determines the running resistance of the vehicle by statistically processing the stored information on the frequency distribution of running resistance. Statistical processing may be, for example, a process of calculating the average value of the frequency distribution of running resistance, or a process of determining which value of the frequency distribution of running resistance to use based on weather information. It may also be a process of Processing that uses weather information is, for example, processing that uses a value larger than the average value among the values of the frequency distribution in the case of rainy weather.

If the statistical information on the running resistance of the vehicle is stored in the server device 62 in this way, the parameter acquisition unit 310 can acquire the statistical information on the running resistance of the vehicle from the server device 62. After that, an affirmative determination is made in the process of step S105. In this case, as the process in step S106, statistical information on the running resistance of the vehicle 10 is acquired from the database of the server device 62, and after substituting the acquired running resistance statistical value Frc into the running resistance setting value Fr, as shown in FIG. Finish the process shown in .

On the other hand, if the information regarding running resistance has not been sufficiently compiled into a database in the server device 62, or if the information regarding running resistance has not been compiled into a database in the server device 62 in the first place, the parameter acquisition unit 310 acquires information about the vehicle 10 from the server device 62. Unable to obtain running resistance information. In this case, the parameter acquisition unit 310 makes a negative determination in step S105, and in the subsequent step S107, substitutes the running resistance fixed value Frd stored in the storage device 42 into the running resistance set value Fr. After that, the process shown in FIG. 3 ends. The running resistance fixed value Frd is a value stored in advance in the storage device 42 as a running resistance value of the vehicle 10.

It is considered that among the values used for the vehicle weight set value m, the vehicle weight input value ma has the highest accuracy, and the accuracy decreases in the order of the vehicle weight estimated value mb and the vehicle weight fixed value mc. Therefore, in the process shown in FIG. 3, the priority order used for the vehicle weight set value m is the vehicle weight input value ma, the estimated vehicle weight value mb, and the vehicle weight fixed value mc, in this order. Similarly, in the process shown in FIG. 3, the priority used for the running resistance set value Fr is the running resistance input value Fra, the running resistance estimated value Frb, the running resistance statistical value Frc, and the running resistance fixed value Frd in the lower order. ing.

After completing the process shown in FIG. 3, the parameter acquisition unit 310 executes an environmental parameter acquisition process as a process in step S11 following step S10, as shown in FIG. The specific procedure of this process is as shown in FIG.
When the parameter acquisition unit 310 starts the environmental parameter acquisition process shown in FIG. 4, first, as a process in step S110, it determines whether weather information on the predicted travel route of the vehicle 10 can be acquired from the server device 62. do. The weather information acquired from the server device 62 includes information such as wind speed, weather, and outside temperature at each point on the predicted travel route of the vehicle 10. If weather information on the predicted travel route of the vehicle 10 can be acquired from the server device 62, the parameter acquisition unit 310 makes an affirmative determination in the process of step S110. In this case, the parameter acquisition unit 310 creates a wind force map M_Fw based on information on the wind speed at each point on the predicted travel route of the vehicle 10, as the process of step S111. Specifically, the parameter acquisition unit 310 calculates the wind force Fw, which is the force that the vehicle 10 receives due to the influence of the wind, at each point based on the information on the wind speed at each point obtained from the server device 62, using a calculation formula, a map, etc. A wind force map M_Fw is created by calculating the wind force map M_Fw. The calculation formula and map for calculating the wind force Fw from the wind speed are determined in advance through experiments and the like. The wind force map M_Fw is a map showing the relationship between each point on the predicted travel route and the wind force Fw that the vehicle 10 receives at each point.

As processing in step S112 following step S111, the parameter acquisition unit 310 creates an air conditioning power map M_Pa based on information on the weather and outside temperature at each point on the predicted travel route of the vehicle 10. Specifically, the parameter acquisition unit 310 calculates the air conditioning power Pa, which is the output of the air conditioner of the vehicle 10 at each point, based on the information on the weather and outside temperature at each point obtained from the server device 62. This calculation is performed using the map shown in FIG. 5, for example.

The map shown in FIG. 5 shows the relationship between the outside temperature Tout and the air conditioning power Pa for each case when the weather is "sunny," "cloudy," and "rainy." , and is stored in the ROM of the prediction ECU 31 in advance. In FIG. 5, a map corresponding to sunny weather is shown by a solid line, a map corresponding to cloudy weather is shown by a dashed-dotted line, and a map corresponding to rainy weather is shown by a dashed-dotted line.

As shown in FIG. 5, when the outside temperature Tout is high, the air conditioner is driven to cool the interior of the vehicle, so air conditioning power Pa is required. Then, as the outside temperature Tout becomes higher, the air conditioning power Pa for cooling becomes larger. Furthermore, when the outside temperature Tout is low, the air conditioner is driven to heat the vehicle interior, so air conditioning power Pa is required. The lower the outside temperature Tout is, the greater the air conditioning power Pa for heating becomes.

On the other hand, when the air conditioner is in a cooling operation on a clear day, the air conditioning power Pa for cooling becomes larger due to the influence of solar radiation on a clear day. Further, when the air conditioner is in heating operation on a clear day, the same level of comfort can be achieved even with a smaller air conditioning power Pa due to the influence of solar radiation on a clear day. In this way, the air conditioning power Pa changes depending on the weather condition.

The map shown in FIG. 5 is created in advance by determining the air conditioning power Pa according to the outside temperature Tout and weather conditions as described above through experiments or the like.
In the process of step S112 shown in FIG. 4, the parameter acquisition unit 310 creates a map corresponding to "clear skies" and a map corresponding to "cloudy skies" shown in FIG. The corresponding map and the map corresponding to "rainy weather" are selected, and the air conditioning power Pa is calculated from the information on the outside temperature at that point using the selected map. The parameter acquisition unit 310 creates an air conditioning power map M_Pa by calculating the air conditioning power Pa for each point on the predicted travel route of the vehicle 10. The air conditioning power map M_Pa is a map of the relationship between each point on the predicted travel route and the air conditioning power Pa of the vehicle 10 at each point. After executing the process of step S112, the parameter acquisition unit 310 ends the process shown in FIG. 4.

As shown in FIG. 4, when parameter acquisition unit 310 determines that weather information on the predicted travel route of vehicle 10 cannot be acquired from server device 62, it makes a negative determination in step S110. In this case, the parameter acquisition unit 310 creates a wind force map M_Fw using the wind force Fw that the vehicle 10 is experiencing at its current location, as the process of step S113.

Specifically, the driving force F of the vehicle 10 can be determined by the following equation f1. In addition, in formula f1, "m" indicates the vehicle weight, "v" indicates the running speed of the vehicle, "g" indicates the gravitational acceleration, "θ" indicates the road surface slope, and "Fr" indicates the vehicle 10 "Fw" indicates the wind force that the vehicle 10 is receiving.

式ｆ１において、車両１０の現在の駆動力Ｆは、ＥＶＥＣＵ３０から取得可能なモータジェネレータ２０の現在の出力の情報から求めることができる。また、車両重量ｍ及び走行抵抗Ｆｒは、図３に示される処理で演算される値を用いることができる。さらに、車両の現在の走行速度ｖは、車載センサ４０の一つである車速センサの検出値を用いることができる。また、路面勾配θは、記憶装置４２に記憶されている地図情報から取得することができる。パラメータ取得部３１０は、これらの取得可能な値を用いることにより、上記の式ｆ１から、車両１０が受けている現在の風力Ｆｗを演算する。パラメータ取得部３１０は、このようにして演算した現在の風力Ｆｗを、車両１０の予測走行経路上の各地点の風力として用いることにより風力マップＭ＿Ｆｗを作成する。

In formula f1, the current driving force F of the vehicle 10 can be determined from information on the current output of the motor generator 20 that can be obtained from the EVECU 30. In addition, for the vehicle weight m and running resistance Fr, values calculated by the process shown in FIG. 3 can be used. Further, as the current traveling speed v of the vehicle, a detected value of a vehicle speed sensor, which is one of the on-vehicle sensors 40, can be used. Further, the road surface slope θ can be obtained from map information stored in the storage device 42. By using these obtainable values, the parameter acquisition unit 310 calculates the current wind force Fw that the vehicle 10 is receiving from the above equation f1. The parameter acquisition unit 310 creates a wind force map M_Fw by using the current wind force Fw calculated in this way as the wind force at each point on the predicted travel route of the vehicle 10.

Furthermore, the parameter acquisition unit 310 creates an air conditioning power map M_Pa based on the current air conditioning power Pa of the vehicle 10 as a process in step S114 following step S113. Specifically, the parameter acquisition unit 310 acquires current air conditioning power information from the air conditioning ECU. Then, the parameter acquisition unit 310 creates an air conditioning power map M_Pa by using the acquired current air conditioning power as the air conditioning power at each point on the predicted travel route of the vehicle 10. After executing the process of step S114, the parameter acquisition unit 310 ends the process shown in FIG. 4.

Note that the wind force map M_Fw created based on the wind speed information at each point acquired from the server device 62 is considered to be more accurate than the wind power map M_Fw created based on the wind speed at the current location of the vehicle. Therefore, in the process shown in FIG. 4, the former is preferentially used. Similarly, in the process shown in FIG. 4, the air conditioning power map M_Pa created based on the weather and outside temperature information at each point acquired from the server device 62 is created based on the air conditioning power Pa at the current location of the vehicle. It is designed to be used preferentially over the air conditioning power map M_Pa.

After completing the process shown in FIG. 4, the parameter acquisition unit 310 executes a map information acquisition process as a process in step S12 following step S11, as shown in FIG. The specific procedure of this process is as shown in FIG.
When the parameter acquisition unit 310 starts the map information acquisition process shown in FIG. 6, first in step S120 it determines whether there is map information that can be used offline. For example, if map information is stored in the storage device 42, the parameter acquisition unit 310 makes a positive determination in step S120. In this case, the parameter acquisition unit 310 creates the slope map M_θ and the upper limit vehicle speed map M_vmax based on the map information stored in the storage device 42 as the process of step S121. The slope map M_θ is a map of the relationship between each point on the predicted travel route and the road surface slope θ at each point. The upper limit vehicle speed map M_vmax is a map of the relationship between each point on the predicted travel route and the upper limit vehicle speed vmax at each point. For example, a legal speed is used as the upper limit vehicle speed vmax. After creating the gradient map M_θ and the upper limit vehicle speed map M_vmax in the process of step S121, the parameter acquisition unit 310 ends the process shown in FIG. 6.

If the parameter acquisition unit 310 determines that there is no map information that can be used offline, it makes a negative determination in the process of step S120, and in the subsequent process of step S122, the parameter acquisition unit 310 determines that there is no map information that can be used offline. Determine whether possible map information exists. For example, when the parameter acquisition unit 310 is in a state where it can communicate with the server device 62 via the communication unit 41 and the map information is stored in the server device 62, the parameter acquisition unit 310 acquires map information that can be used online. is determined to exist, and an affirmative determination is made in the process of step S122. In this case, as the processing in step S123, the parameter acquisition unit 310 acquires information on the road surface gradient and upper limit vehicle speed at each point on the predicted travel route from the server device 62, and then based on the information, the parameter acquisition unit 310 obtains the slope map M_θ and the upper limit vehicle speed. A vehicle speed map M_vmax is created and the process shown in FIG. 6 is ended.

If the parameter acquisition unit 310 determines that there is no map information that can be used online, it makes a negative determination in step S122. In this case, the parameter acquisition unit 310 determines whether information on the estimated values of the road surface slope and the upper limit vehicle speed exists in the server device 62 as the process of step S124. For example, if the server device 62 stores information on the driving force of the power source of the other vehicles at each point and information on the traveling speed in a database as information on the past travel history of the other vehicles, The server device 62 can calculate respective estimated values of the road surface slope and the upper limit vehicle speed.

Specifically, if the information on the traveling speed of other vehicles at each point stored in a database in the server device 62 includes information on the upper limit vehicle speed at each point, the prediction of the vehicle 10 is made based on that information. An estimated value of the upper limit vehicle speed at each point on the travel route can be obtained. In addition, if information on the driving force and traveling speed of the power source of other vehicles at each point is stored in a database in the server device 62, the server device 62 can calculate the driving force of the vehicle by using the above equation f1 from that information. It is possible to calculate the estimated value of the road surface slope at each point on the 10 predicted travel routes.

Note that the server device 62 may calculate the road surface slope and the upper limit vehicle speed by statistically processing the traveling speed of other vehicles. The statistical process is, for example, a process that calculates the average value of past upper limit speeds of other vehicles.
Furthermore, even if the server device 62 does not have data on each point on the predicted travel route of the vehicle 10, if data on surrounding points on the predicted travel route exists in the server device 62, that data is used. As a result, estimated values of the road surface slope and the upper limit vehicle speed can be calculated in the same way.

If the estimated values of the road surface slope and the upper limit vehicle speed at each point are stored in the server device 62, the parameter acquisition unit 310 makes an affirmative determination in the process of step S124. In this case, as the process of step S125, the parameter acquisition unit 310 acquires information on the estimated value of the road surface gradient and the estimated value of the upper limit vehicle speed at each point on the predicted travel route from the server device 62, and then acquires the information from the server device 62. A gradient map M_θ and an upper limit vehicle speed map M_vmax are created based on the obtained information, and the process shown in FIG. 6 is completed.

If the estimated values of the road surface slope and the upper limit vehicle speed at each point are not stored in the server device 62, the parameter acquisition unit 310 makes a negative determination in the process of step S124. In this case, the parameter acquisition unit 310 creates the slope map M_θ and the upper limit vehicle speed map M_vmax based on the information stored in the storage device 42 of the host vehicle 10 as the process of step S126.

Specifically, the parameter acquisition unit 310 reads information on the past driving force and running speed of the own vehicle 10 at each point from the storage device 42 . Then, the parameter acquisition unit 310 calculates the estimated value of the upper limit vehicle speed by statistically processing the past traveling speed of the own vehicle 10 at each point. The statistical process is, for example, a process that calculates the average value of the past upper limit speeds of the vehicle 10. Further, the parameter acquisition unit 310 calculates the road surface gradient at each point using the above formula f1 from the past history of the driving force of the motor generator 20 at each point, the past history of the traveling speed of the vehicle 10, and the like. Note that statistical processing may also be used for the processing of calculating the road surface slope. The parameter acquisition unit 310 creates the slope map M_θ and the upper limit vehicle speed map M_vmax based on the estimated value of the road surface slope and the estimated value of the upper limit vehicle speed calculated in this way, and then ends the process shown in FIG. 6.

Note that if the vehicle 10 has not traveled on the current predicted travel route in the past, information on the past driving force and travel speed of the vehicle 10 at each point on the predicted travel route is stored in the storage device 42. There may be cases where it is not. In such a case, if information on the past driving force and traveling speed of the vehicle 10 at surrounding points of the predicted travel route is stored in the storage device 42, the parameter acquisition unit 310 uses that information to The estimated value of the road surface slope and the estimated value of the upper limit vehicle speed at each point on the predicted travel route may be calculated. In addition, if information on the past driving force and traveling speed of the vehicle 10 at peripheral points on the predicted travel route is not stored in the storage device 42, the parameter acquisition unit 310 determines the road surface gradient at each point on the predicted travel route. Fixed values may be used as the estimated value and the estimated value of the upper limit vehicle speed.

When the parameter acquisition section 310 finishes the process shown in FIG. 6, that is, when the parameter acquisition section 310 finishes the process of steps S10 to S12 shown in FIG. mc, running resistance setting value Fr, wind force map M_Fw, air conditioning power map M_Pa, gradient map M_θ, and upper limit vehicle speed map M_vmax are acquired. In the present embodiment, these parameters correspond to predetermined parameters necessary to calculate the cruising distance of the vehicle 10 separately from the information regarding the driving route and the information regarding the remaining energy amount. After the parameter acquisition unit 310 acquires each parameter in this manner, the calculation unit 313 executes the processing from step S13 shown in FIG. 2 to calculate and display the possible cruising distance.

Specifically, as processing in step S13, the calculation unit 313 creates a vehicle speed profile v(x) indicating a transition in the future traveling speed of the vehicle 10. For example, based on the upper limit vehicle speed map M_vmax calculated through the process shown in FIG. 6 and information on the stopping location of the vehicle 10, the calculation unit 313 creates a final upper limit vehicle speed map as shown by the solid line in FIG. Create MF_vmax.

As shown in FIG. 7, the calculation unit 313 transforms the upper limit vehicle speed map M_vmax so that it becomes "0 [km/h]" at the predetermined stopping point Ps, thereby obtaining the final speed shown by the solid line. An upper limit vehicle speed map MF_vmax is created. When the position information of the traffic light is stored in the storage device 42, the calculation unit 313 determines the position of the traffic light, which is determined at random using random numbers, as the stop point Ps among all the traffic lights. Note that "random" is defined on the condition that, for example, 50% of all traffic lights are red. Further, the calculation unit 313 may determine the stop point Ps based on the traffic light cycle information at the current time. The stop time at the traffic light may be a fixed time such as 60 seconds, or may be determined based on past driving history. Furthermore, the calculation unit 313 may obtain and use traffic signal cycle information at the current time using the communication unit 41.

After creating the final upper limit vehicle speed map MF_vmax in this manner, the calculation unit 313 creates a vehicle speed profile v(x) that satisfies this. For example, the calculation unit 313 calculates the vehicle speed profile v(x ) may be created. Furthermore, the calculation unit 313 may create a vehicle speed profile v(x) that satisfies the upper limit vehicle speed map M_vmax using traffic flow simulation. Through the above-described calculations, the calculation unit 313 creates a vehicle speed profile v(x) indicating changes in vehicle speed at each point on the predicted travel route of the vehicle 10, as shown by the two-dot chain line in FIG. Note that "x" indicates the distance from the current location to a predetermined point on the predicted travel route of the vehicle 10.

As shown in FIG. 2, the calculation unit 313 creates a driving force profile F(x) as a process in step S14 following step S13. Specifically, the calculation unit 313 calculates the vehicle weight setting value m and running resistance setting value Fr calculated through the process shown in FIG. 3, the wind force map M_Fw calculated through the process shown in FIG. The driving force F at each point on the travel route of the vehicle 10 is calculated using the above formula f1 from the gradient map M_θ calculated through the process of step S13 and the vehicle speed profile v(x) calculated through the process of step S13. . Thereby, the calculation unit 313 creates a driving force profile F(x) that indicates the transition of the driving force at each point on the travel route of the vehicle 10.

The calculation unit 313 creates a drive power profile Pd(x) as processing in step S15 following step S14. Specifically, the calculation unit 313 calculates the driving power profile Pd(x) based on the following equation f2 from the driving force profile F(x) created in the process of step S14 and the vehicle speed profile v(x). create.

演算部３１３は、ステップＳ１５に続くステップＳ１６の処理として、空調パワープロフィールＰａ（ｘ）を作成する。具体的には、演算部３１３は、図４に示される処理を通じて演算される空調パワーマップＭ＿Ｐａに基づいて空調パワープロフィールＰａ（ｘ）を作成する。

The calculation unit 313 creates an air conditioning power profile Pa(x) as processing in step S16 following step S15. Specifically, the calculation unit 313 creates an air conditioning power profile Pa(x) based on the air conditioning power map M_Pa calculated through the process shown in FIG.

The calculation unit 313 creates a power consumption profile Pb(x) of the battery 22 as processing in step S17 following step S16. Specifically, the calculation unit 313 calculates the following equation based on the drive power profile Pd(x) obtained in the process of step S15 and the air conditioning power profile Pa(x) obtained in the process of step S16. A power consumption profile Pb(x) of the battery 22 is created. Note that in equation f3, "η_d" indicates the energy efficiency of the drive system, and "η_a" indicates the energy efficiency of the air conditioner. As the energy efficiency η_d of the drive system, for example, the value that the parameter acquisition unit 310 acquires from the EVECU 30 is used. As the energy efficiency η_a of the air conditioner, for example, the one that the parameter acquisition unit 310 acquires from the air conditioner is used.

演算部３１３は、ステップＳ１７に続くステップＳ１８の処理として、車両１０の限界到達地点Ｋｂを探索する。具体的には、演算部３１３は、エネルギ残量取得部３１２により取得されているバッテリ２２の現在のＳＯＣ値と、ステップＳ１７の処理で得られるバッテリ２２の消費パワープロフィールＰｂ（ｘ）とに基づいて、図８に示されるようなバッテリ２２のＳＯＣ値の推移を作成する。そして、演算部３１３は、バッテリ２２のＳＯＣ値が所定の閾値に達する地点を車両１０の限界到達地点Ｋｂと判断するとともに、車両１０の現在地Ｋａから限界到達地点Ｋｂまでの距離を航続可能距離Ｄと判断する。

The calculation unit 313 searches for the limit reaching point Kb of the vehicle 10 as processing in step S18 following step S17. Specifically, the calculation unit 313 calculates the power consumption profile Pb(x) of the battery 22 based on the current SOC value of the battery 22 acquired by the remaining energy acquisition unit 312 and the power consumption profile Pb(x) of the battery 22 obtained in the process of step S17. Then, the transition of the SOC value of the battery 22 as shown in FIG. 8 is created. Then, the calculation unit 313 determines the point where the SOC value of the battery 22 reaches a predetermined threshold value as the limit reaching point Kb of the vehicle 10, and calculates the cruising distance D from the current location Ka of the vehicle 10 to the limit reaching point Kb. I judge that.

As shown in FIG. 2, as a process in step S19 following step S18, the calculation unit 313 determines whether or not the limit reaching point Kb has been calculated for all of the predicted travel routes on which the vehicle 10 may travel. do. If there is a predicted travel route for which the limit reaching point Kb has not been calculated, the calculation unit 313 makes a negative determination in the process of step S19, and returns to the process of step S12.

On the other hand, when the calculation unit 313 calculates the limit reaching point Kb for all the predicted travel routes, it makes an affirmative determination in the process of step S19. In this case, the calculation unit 313 instructs the HMIECU 32 to display map information around the own vehicle 10 as the process of step S20. Thereby, the HMIECU 32 displays map information around the vehicle 10 on the HMI device 50.

Subsequently, as processing in step S21, the calculation unit 313 instructs the HMIECU 32 to display the limit reaching point Kb of each predicted travel route. As a result, the HMIECU 32 displays the limit reaching points Kb11 to Kb19 of each predicted travel route on the HMI device 50, as shown in FIG. m19 is also displayed. The area surrounded by straight lines m11 to m19 indicates an area that the vehicle 10 can reach. Therefore, the driver of the vehicle 10 can grasp the possible cruising distance of the vehicle 10 by looking at the straight lines m11 to m19 displayed on the HMI device 50.

According to the predictive ECU 31 of the present embodiment described above, it is possible to obtain the actions and effects shown in (1) to (7) below.
(1) The calculation unit 313 uses information regarding the driving route acquired by the route information acquisition unit 311, information regarding the SOC value of the battery 22 acquired by the remaining energy level acquisition unit 312, and information about the vehicle acquired by the parameter acquisition unit 310. The possible cruising distance D of the vehicle 10 is calculated based on predetermined parameters such as the weight setting value m. For example, if the vehicle weight setting value m and running resistance setting value Fr cannot be set by the process of step S101 shown in FIG. Set the weight setting value m and running resistance setting value Fr. In this case, the processing in step S101 corresponds to the first acquisition means, and the processing in steps S103 and S106 corresponds to the second acquisition means. Regarding the wind power map M_Fw and the air conditioning power map M_Pa, the processing in steps S111 and S112 shown in FIG. 4 corresponds to the first acquisition means, and the processing in steps S113 and S114 corresponds to the second acquisition means. Furthermore, regarding the gradient map M_θ and the upper limit vehicle speed map M_vmax, the processing in step S121 shown in FIG. 6 corresponds to the first acquisition means, and the processing in steps S123, S125, and S126 corresponds to the second acquisition means. According to this configuration, even if the first acquisition means cannot acquire a predetermined parameter such as the vehicle weight set value m, the second acquisition means can acquire the predetermined parameter. It becomes possible to ensure redundancy regarding calculations.

(2) The parameter acquisition unit 310, as a second acquisition means for acquiring the vehicle weight set value m and running resistance set value Fr, executes the process of step S103 shown in FIG. A vehicle weight set value m and a running resistance set value Fr are calculated based on own vehicle detection information such as the detectable running speed of the vehicle 10 and the driving force of the motor generator 20. Regarding the wind power map M_Fw and the air conditioning power map M_Pa, the processes in steps S113 and S114 shown in FIG. 4 correspond to similar processes. Further, regarding the slope map M_θ and the upper limit vehicle speed map M_vmax, the process in step S126 shown in FIG. 6 corresponds to the same process. According to this configuration, each parameter is calculated based on information detected in the own vehicle 10, so that the calculation accuracy of each parameter can be ensured.

(3) The process of step S126 shown in FIG. 6 is based on information regarding the past driving history of the own vehicle 10, such as the past running speed history of the own vehicle 10 and the past driving force history of the motor generator 20. This is a process of calculating a gradient map M_θ and an upper limit vehicle speed map M_vmax. Specifically, as the processing in step S126, the parameter acquisition unit 310 statistically processes the history of the past running speed of the own vehicle 10, the history of the past driving force of the motor generator 20, etc. to obtain the gradient map M_θ. and calculates an upper limit vehicle speed map M_vmax. In this embodiment, the past traveling speed of the host vehicle 10 and the past driving force of the motor generator 20 correspond to the past host vehicle detection information. According to this configuration, even if the gradient map M_θ and the upper limit vehicle speed map M_vmax cannot be obtained using the process of step S121, they can be calculated with high accuracy.

(4) The parameter acquisition unit 310 executes the process of step S106 shown in FIG. The running resistance setting value Fr is set using the running resistance information. Further, the parameter acquisition unit 310 serves as a second acquisition means for acquiring the slope map M_θ and the upper limit vehicle speed map M_vmax, which are stored in the server device 62 by executing the process of step S125 shown in FIG. A slope map M_θ and an upper limit vehicle speed map M_vmax are acquired. The server device 62 creates a gradient map M_θ and an upper limit vehicle speed map M_vmax based on information that can be obtained from a plurality of other vehicles other than the vehicle 10. According to this configuration, since each parameter is calculated based on information about other vehicles, the calculation accuracy of each parameter can be ensured.

(5) If the parameter acquisition unit 310 cannot execute the process of step S103 shown in FIG. By executing this, the running resistance set value Fr is set using information on the running resistance of other vehicles. According to this configuration, the running resistance set value Fr is preferentially set based on the detection information of the own vehicle, so that the accuracy of the running resistance set value Fr can be ensured.

(6) The gradient map M_θ and the upper limit vehicle speed map M_vmax created in the process of step S125 shown in FIG. It is created based on data that is statistically processed. According to this configuration, even if the slope map M_θ and the upper limit vehicle speed map M_vmax cannot be obtained using the processes of steps S121 and S123, the slope map M_θ and the upper limit vehicle speed map M_vmax can be calculated with high accuracy. Can be done.

(7) If the vehicle weight setting value m cannot be set in the processing of steps S101 and S103 shown in FIG. 3, the parameter acquisition unit 310 sets the vehicle weight setting value m to a fixed value mc as the processing of step S104. do. Further, if the running resistance setting value Fr cannot be set in the processing of steps S101, S103, and S106 shown in FIG. Set. According to this configuration, it is possible to avoid a situation in which the vehicle weight set value m and running resistance set value Fr cannot be obtained, so that the possible cruising distance D and the limit reaching point Kb can be calculated more reliably.

Note that the above embodiment can also be implemented in the following forms.
- The method of displaying the possible cruising distance D on the HMI device 50 can be changed as appropriate. For example, the possible cruising distance D may be displayed numerically on the HMI device 50.

- The configuration of the predictive ECU 31 of the above embodiment is applicable not only to the vehicle 10 that uses the motor generator 20 as a power source, but also to an engine vehicle that uses an engine as a power source, and a hybrid vehicle that uses both a motor generator and an engine as a power source. Applicable. Note that when the predictive ECU 31 of the embodiment described above is applied to an engine vehicle, the information regarding the remaining amount of energy that can be used for driving the vehicle is, for example, information regarding the remaining amount of fuel.

- The predictive ECU 31 and its control method described in the present disclosure are provided by configuring a processor and memory programmed to execute one or more functions embodied by a computer program. It may be realized by a dedicated computer. The predictive ECU 31 and its control method described in the present disclosure may be implemented by a dedicated computer provided by configuring a processor including one or more dedicated hardware logic circuits. The predictive ECU 31 and the control method thereof described in the present disclosure are configured by a combination of a processor and memory programmed to execute one or more functions and a processor including one or more hardware logic circuits. It may be implemented by one or more special purpose computers. A computer program may be stored as instructions executed by a computer on a computer-readable non-transitory tangible storage medium. Dedicated hardware logic circuits and hardware logic circuits may be implemented by digital circuits that include multiple logic circuits, or by analog circuits.

- The present disclosure is not limited to the above specific examples. Design changes made by those skilled in the art to the specific examples described above are also included within the scope of the present disclosure as long as they have the characteristics of the present disclosure. The elements included in each of the specific examples described above, as well as their arrangement, conditions, shapes, etc., are not limited to those illustrated, and can be changed as appropriate. The elements included in each of the specific examples described above can be appropriately combined as long as no technical contradiction occurs.

31: Prediction ECU (calculation unit)
310: Parameter acquisition unit 311: Route information acquisition unit 312: Remaining energy acquisition unit 313: Calculation unit

Claims (9)

A computing device (31) that computes the cruising range of a vehicle,
a route information acquisition unit (311) that acquires information regarding the travel route of the vehicle;
an energy remaining amount obtaining unit (312) that obtains information regarding the remaining amount of energy that can be used for driving the vehicle;
a parameter acquisition unit (310) that acquires a predetermined parameter necessary for calculating the possible cruising distance apart from the information regarding the driving route and the information regarding the remaining amount of energy;
a calculation unit (313) that calculates the possible cruising distance of the vehicle based on the information regarding the travel route, the information regarding the remaining amount of energy, and the predetermined parameters;
The parameter acquisition unit includes:
If the predetermined parameter cannot be acquired by the first acquisition means, the predetermined parameter is acquired by a second acquisition means different from the first acquisition means ;
The second acquisition means sets the predetermined parameters based on own vehicle detection information that is information detectable in the own vehicle,
The second acquisition means sets the predetermined parameters based on information acquired from another vehicle when the predetermined parameters cannot be calculated from the own vehicle detection information.
Vehicle computing device. The vehicle arithmetic device according to claim 1 , wherein the parameter acquisition unit uses past own vehicle detection information as the own vehicle detection information. The parameter acquisition unit includes:
Using information regarding the past driving history of the own vehicle as the past own vehicle detection information,
The vehicle arithmetic device according to claim 2 , wherein the second acquisition means sets the predetermined parameter based on a value obtained by statistically processing information regarding the past driving history of the host vehicle. The vehicle arithmetic device according to claim 3 , wherein the information regarding the past travel history of the host vehicle includes at least one of information on the travel speed of the host vehicle and information on the driving force of the power source of the host vehicle. . The vehicle arithmetic device according to any one of claims 1 to 4 , wherein the information acquired from the other vehicle is information detected in the other vehicle. The vehicle arithmetic device according to claim 5 , wherein the parameter acquisition unit sets the predetermined parameter by statistically processing information detected in a plurality of other vehicles. The vehicle arithmetic device according to claim 6 , wherein the information detected in the other vehicle includes at least one of information on the traveling speed of the other vehicle and information on the driving force of the power source of the other vehicle. The vehicle according to any one of claims 1 to 7 , wherein the parameter acquisition unit uses a predetermined value as the predetermined parameter when the second acquisition unit cannot acquire the predetermined parameter. Computing device. At least one processing unit (31),
Obtain information about the vehicle's driving route,
obtaining information regarding the remaining amount of energy that can be used for driving the vehicle;
Acquiring a predetermined parameter necessary for calculating the cruising distance of the vehicle separately from the information regarding the driving route and the information regarding the remaining amount of energy;
Calculating the possible cruising distance of the vehicle based on information regarding the travel route, information regarding the remaining amount of energy, and the predetermined parameter;
If the predetermined parameter cannot be acquired by the first acquisition means, the predetermined parameter is acquired by a second acquisition means different from the first acquisition means;
The second acquisition means sets the predetermined parameters based on own vehicle detection information that is information detectable in the own vehicle;
The second acquisition means causes the predetermined parameter to be set based on information acquired from another vehicle when the predetermined parameter cannot be calculated from the own vehicle detection information.
program.

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