Processing device

Abstract

This processing device comprises one or a plurality of processors and one or a plurality of memories connected to the processor. The processor executes processing including: obtaining pattern information which is information indicating an operation pattern of at least one traffic light existing in a travel route of a vehicle; and predicting, on the basis of the pattern information, the total consumption amount of an energy source of the vehicle in a case where the vehicle travels on the travel route.

Description

Technical Field

[0001] The present invention relates to a processing device.

Background Art

[0002] Vehicles consume an energy source mounted on the vehicle to run. For example, in an electric vehicle, the electric power stored in a battery is used as an energy source. And, in order to assist the driving of the vehicle driver, it is important to predict the consumption amount of the energy source of the vehicle. For example, Patent Document 1 discloses a technique for predicting the cruising range of a vehicle by predicting the consumption amount of the energy source of the vehicle.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As described above, various techniques for predicting the consumption amount of the energy source of a vehicle have been proposed in order to assist the driving of the vehicle driver. However, further techniques for more accurately predicting the consumption amount of the energy source of the vehicle are desired.

[0005] Therefore, an object of the present invention is to provide a processing device capable of accurately predicting the consumption amount of the energy source of a vehicle.

Means for Solving the Problems

[0006] In order to solve the above problems, a processing device according to an embodiment of the present invention includes: one or more processors; one or more memories connected to the processor; and has The processor acquires pattern information, which is information indicating an operation pattern of at least one traffic signal existing on a driving route of the vehicle, Predicting the number of stops, which is the number of times the vehicle stops at the traffic signal, based on the pattern information; and predicts a total consumption amount of an energy source of the vehicle when the vehicle travels on the driving route The number of stops based on the pattern information, and executes a process including this.

Advantages of the Invention

[0007] According to the present invention, it becomes possible to accurately predict the consumption amount of the energy source of the vehicle.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0009] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The specific dimensions, materials, numerical values, etc. shown in such embodiments are merely examples for facilitating the understanding of the invention and do not limit the present invention unless otherwise specified. In the present specification and drawings, elements having substantially the same functions and configurations are denoted by the same reference numerals to omit redundant descriptions, and elements not directly related to the present invention are not shown.

[0010] <Configuration of Vehicle> With reference to FIGS. 1 and 2, the configuration of a vehicle 1 according to an embodiment of the present invention will be described.

[0011] FIG. 1 is a schematic diagram showing a schematic configuration of the vehicle 1. Hereinafter, an example will be described in which the vehicle 1 is an electric vehicle equipped with a battery 21 and travels using the electric power stored in the battery 21 as an energy source. That is, in the following example, the driving distance per unit capacity of the energy source is the power consumption rate.

[0012] However, the energy source of the vehicle 1 is not limited to the electric power stored in the battery 21. For example, the vehicle 1 may be an engine vehicle equipped with an engine and may travel using the fuel supplied to the engine as an energy source. In that case, the driving distance per unit capacity of the energy source is the fuel consumption.

[0013] As shown in FIG. 1, the vehicle 1 includes a communication device 11, a battery 21, a driving motor 22, a navigation device 23, and a processing device 30.

[0014] The communication device 11 communicates with devices outside the vehicle 1.

[0015] The battery 21 can charge and discharge electric power. As the battery 21, for example, a lithium-ion battery, a lithium-ion polymer battery, a nickel-metal hydride battery, a nickel-cadmium battery, or a lead storage battery is used, but other batteries may be used. The battery 21 stores the electric power supplied to the driving motor 22.

[0016] The traveling motor 22 outputs power transmitted to the wheels of the vehicle 1 and is composed of, for example, a three-phase alternating current motor. The traveling motor 22 is driven using the power of the battery 21 to output power. Further, the traveling motor 22 can be regeneratively driven during deceleration of the vehicle 1 to generate electricity using the kinetic energy of the wheels. In this case, the power generated by the traveling motor 22 is supplied to the battery 21. Thereby, the battery 21 is charged by the power generated by the traveling motor 22.

[0017] The navigation device 23 is a device that guides a driving route from the current location of the vehicle 1 to the destination desired by the user in response to an input operation by the driver of the vehicle 1. The navigation device 23 determines a recommended driving route from among a plurality of driving route candidates and proposes the determined recommended driving route to the user. In this specification, the recommended driving route is a driving route in which the consumption of the energy source is suppressed. The driver selects a driving route to be guided by the navigation device 23 from among a plurality of driving route candidates with reference to the recommended driving route.

[0018] Specifically, the navigation device 23 has a function of visually displaying information and displays various information related to route guidance. Examples of the information displayed by the navigation device 23 include the current location of the vehicle 1, the driving route to be guided, the location of the destination, the distance on the driving route from the current location of the vehicle 1 to the destination, and the arrival time to the destination. The navigation device 23 can acquire information indicating the current location of the vehicle 1 based on a signal transmitted from a GPS (Global Positioning System) satellite.

[0019] The processing device 30 includes one or more processors 30a and one or more memories 30b connected to the processor 30a. The processor 30a includes, for example, a CPU (Central Processing Unit). The memory 30b includes, for example, a ROM (Read Only Memory) and a RAM (Random Access Memory). The ROM is a storage element that stores programs and arithmetic parameters used by the CPU. The RAM is a storage element that temporarily stores data such as variables and parameters used in the processes executed by the CPU.

[0020] The processing device 30 communicates with each device in the vehicle 1 such as the communication device 11 and the navigation device 23. The communication between the processing device 30 and each device is realized, for example, using CAN (Controller Area Network) communication.

[0021] FIG. 2 is a block diagram showing an example of the functional configuration of the processing device 30. For example, as shown in FIG. 2, the processing device 30 includes an acquisition unit 31 and a processing unit 32. Note that various processes including the processes described below performed by the acquisition unit 31 or the processing unit 32 can be executed by the processor 30a. Specifically, various processes are executed by the processor 30a executing the programs stored in the memory 30b.

[0022] The acquisition unit 31 acquires various information and outputs it to the processing unit 32. For example, the acquisition unit 31 acquires information from the communication device 11 and the navigation device 23. Note that in this specification, the acquisition of information may include the extraction or generation (for example, calculation) of information.

[0023] The processing unit 32 performs various processes using the various information acquired by the acquisition unit 31. The processing unit 32 also has a function of controlling the operation of the navigation device 23.

[0024] Note that the functions of the processing device 30 according to the present embodiment may be divided among a plurality of devices, or a plurality of functions may be realized by one device. When the functions of the processing device 30 are divided among a plurality of devices, the plurality of devices may be connected to each other via a communication bus such as CAN.

[0025] <Operation of the processing device> Subsequently, with reference to FIGS. 3 to 5, the operation of the processing device 30 according to the embodiment of the present invention will be described.

[0026] In the present embodiment, the processing device 30 acquires pattern information, which is information indicating the operation pattern of a traffic signal existing on the travel route of the vehicle 1, and predicts the total consumption amount of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route based on the pattern information. Thereby, as will be described later, it is possible to accurately predict the consumption amount of the energy source of the vehicle 1. In the following example, the energy source is electric power.

[0027] The operation pattern of the traffic signal indicated by the pattern information is, for example, a pattern such as at what timing the color of the traffic signal changes to what color. Examples of the pattern information include information in which the colors of the traffic signal at each time are arranged in time series, or information indicating the cycle in which the color of the traffic signal changes. In the following, an example in which the processing device 30 acquires pattern information indicating the operation patterns of a plurality of traffic signals existing on the travel route of the vehicle 1 will be described. However, the processing device 30 only needs to acquire pattern information indicating the operation pattern of at least one traffic signal existing on the travel route of the vehicle 1.

[0028] Hereinafter, as processing examples performed by the processing device 30, a first example and a second example will be described in order.

[0029] FIG. 3 is a flowchart showing a first example of the flow of processing performed by the processing device 30. The control flow shown in FIG. 3 is repeatedly executed, for example, at a preset time interval.

[0030] When the control flow shown in FIG. 3 starts, first, in step S101, the processing device 30 predicts the total power consumption of each driving route candidate. Specifically, the processing device 30 predicts the total power consumption of the battery 21 when each driving route candidate is traveled. The driving route candidates are extracted by the navigation device 23 based on, for example, the current location of the vehicle 1 and the destination desired by the user. Note that the details of the processing in step S101 will be described later with reference to FIG. 4.

[0031] Next to step S101, in step S102, the processing unit 32 of the processing device 30 determines the recommended driving route, and the control flow shown in FIG. 3 ends. As described above, the recommended driving route is a driving route in which power consumption is suppressed. That is, in step S101, the processing unit 32 determines the driving route candidate with the smallest predicted total power consumption as the recommended driving route. Then, the processing unit 32 notifies the driver of the recommended driving route, for example, by causing the navigation device 23 to display the recommended driving route.

[0032] FIG. 4 is a flowchart showing an example of the flow of the prediction process of the total power consumption performed by the processing device 30. The control flow shown in FIG. 4 is executed in step S101 in FIG. 3.

[0033] Specifically, the control flow shown in FIG. 4 is a control flow for predicting the total power consumption for one driving route. Therefore, in step S101 in FIG. 3, the control flow shown in FIG. 4 is performed in order for all driving route candidates.

[0034] FIG. 5 is a diagram schematically showing a travel route 2 of the vehicle 1. For example, when the travel route 2 shown in FIG. 5 is extracted as a travel route candidate, in the control flow shown in FIG. 4, the total power consumption of the battery 21 when the vehicle 1 travels on the travel route 2 is predicted. As shown in FIG. 5, there are a plurality of traffic lights 3 on the travel route 2. The types of the traffic lights 3 include a centralized control type and a decentralized control type. The centralized control type is installed in a specific range with relatively heavy traffic and is controlled by a computer of a traffic control center. The decentralized control type is installed in a place with relatively light traffic, is not connected to the computer of the traffic control center, and is controlled independently.

[0035] When the control flow shown in FIG. 4 is started, first, in step S201, the acquisition unit 31 acquires pattern information indicating the operation patterns of the plurality of traffic lights 3 existing on the travel route 2.

[0036] In step S201, the acquisition unit 31 acquires the pattern information by different methods according to the type of the traffic light 3, for example. Note that information indicating the number, position, and type of each traffic light 3 on the travel route 2 is stored in the navigation device 23 in advance, for example. For example, the acquisition unit 31 acquires this information from the navigation device 23 when extracting the travel route candidate.

[0037] When the traffic light 3 for which the pattern information is to be acquired is of the centralized control type, for example, the acquisition unit 31 acquires the pattern information of the centralized control type traffic light 3 from the computer of the traffic control center. In this case, the acquisition unit 31 communicates with the computer of the traffic control center via the communication device 11.

[0038] When the traffic light 3 for which the pattern information is to be acquired is of the decentralized control type, for example, the acquisition unit 31 acquires the pattern information of the decentralized control type traffic light 3 from the navigation device 23. In this case, the pattern information of the decentralized control type traffic light 3 is stored in the navigation device 23 in advance.

[0039] Next to step S201, in step S202, the processing unit 32 predicts the number of stops, which is the number of times the vehicle 1 stops at the traffic signal 3, based on the pattern information.

[0040] In step S202, the processing unit 32 performs, for example, a simulation assuming that each traffic signal 3 existing on the travel route 2 operates in the operation pattern indicated by the pattern information and the vehicle 1 accelerates to the legal speed and travels. This simulation is performed based on the number, position, pattern information of the traffic signals 3 existing on the travel route 2, and the legal speed of the travel route 2. In this simulation, the vehicle 1 stops at the red traffic signal 3 and travels without stopping at locations other than the red traffic signal 3. Also, in this simulation, the vehicle 1 accelerates when the vehicle speed is below the legal speed and maintains the vehicle speed when the vehicle speed has reached the legal speed. Also, in this simulation, the vehicle 1 accelerates at a preset acceleration. The acceleration is set, for example, based on the average value of the past actual accelerations of the vehicle 1. Also, in this simulation, the vehicle 1 decelerates at a preset rate of speed change before the red traffic signal 3 so that it can stop at the red traffic signal 3. The rate of speed change is set, for example, based on the average value of the past actual rates of speed change during deceleration of the vehicle 1.

[0041] Based on the result of the simulation, the processing unit 32 can predict the behavior of the vehicle 1 when the vehicle 1 travels on the travel route 2. And in step S202, the processing unit 32 predicts the number of stops based on the result of the simulation. Specifically, in the above-described simulation, since the processing unit 32 can specify the speed of the vehicle 1 at each time point, it can specify the position of the vehicle 1 at each time point. Also, the processing unit 32 can specify the color of each traffic signal 3 at each time point. Therefore, the processing unit 32 can specify the color of the traffic signal 3 when the vehicle 1 reaches the installation position of the traffic signal 3. Thus, the processing unit 32 can specify the traffic signal 3 at which the vehicle 1 stops. For example, in this way, the processing unit 32 can predict the number of stops. Specifically, the processing unit 32 can also predict at which traffic signal 3 the vehicle 1 stops.

[0042] Next to step S202, in step S203, the processing unit 32 predicts the stop time, which is the time for the vehicle 1 to stop at the traffic signal 3, based on the pattern information. When it is predicted that the vehicle 1 will stop at a plurality of traffic signals 3, the processing unit 32 predicts the stop time for each of the plurality of traffic signals 3.

[0043] In step S203, the processing unit 32 predicts the stop time based on, for example, the result of the simulation described above. Specifically, in the above-described simulation, since the processing unit 32 can specify the speed of the vehicle 1 at each time point, it can specify the position of the vehicle 1 at each time point. In addition, the processing unit 32 can specify the color of each traffic signal 3 at each time point. Therefore, when the vehicle 1 reaches a red traffic signal 3 and stops, the processing unit 32 can specify the time from the timing when the vehicle 1 stops to the timing when the color of the traffic signal 3 changes from red to blue and the vehicle 1 starts. For example, in this way, the processing unit 32 can predict the stop time. Specifically, the processing unit 32 can also predict at which traffic signal 3 the vehicle 1 will stop and for how long.

[0044] Next to step S203, in step S204, the processing unit 32 predicts the continuous travel distance that the vehicle 1 continues to travel after stopping at the traffic signal 3 until it stops at the traffic signal 3 next, based on the pattern information. When it is predicted that the vehicle 1 will stop at a plurality of traffic signals 3, the distance between the traffic signals 3 arranged continuously with each other among the plurality of traffic signals 3 (specifically, the distance along the road) corresponds to the continuous travel distance.

[0045] In step S204, the processing unit 32 predicts the continuous travel distance based on, for example, the result of the simulation described above.

[0046] Next to step S204, in step S205, the processing unit 32 predicts the total power consumption of the battery 21 when the vehicle 1 travels on the travel route 2, and the control flow shown in FIG. 4 ends.

[0047] In step S205, the processing unit 32 predicts, for example, the total power consumption based on the number of stops. Here, when the vehicle 1 stops at the traffic signal 3, it becomes necessary to accelerate needlessly due to the stop. Therefore, when the vehicle 1 stops at the traffic signal 3, energy loss occurs due to the unnecessary acceleration and deceleration of the vehicle 1 compared to the case where the vehicle 1 passes through without stopping at the traffic signal 3. Thus, power consumption occurs due to the vehicle 1 stopping at the traffic signal 3. The processing unit 32 can predict, as the total power consumption, a value obtained by multiplying the power consumption generated due to the vehicle 1 stopping once at the traffic signal 3 by the number of stops.

[0048] For example, the power consumption generated due to the vehicle 1 stopping once at the traffic signal 3 is stored in advance in the storage element of the processing device 30. Then, the processing unit 32 multiplies the power consumption generated due to the vehicle 1 stopping once at the traffic signal 3 by the number of stops. Further, the processing unit 32 adds the value thus obtained to the total power consumption assumed when the vehicle 1 travels along the travel route 2 without stopping at the traffic signal 3 even once. Then, the processing unit 32 predicts the finally obtained value as the total power consumption of the battery 21 when the vehicle 1 travels along the travel route 2.

[0049] Note that the power consumption generated due to the vehicle 1 stopping once at the traffic signal 3 is obtained, for example, by subtracting the amount of regenerated power obtained due to the vehicle 1 stopping at the traffic signal 3 from the power consumption generated due to the vehicle 1 accelerating after stopping. The power consumption generated due to the vehicle 1 accelerating after stopping is obtained based on, for example, the target vehicle speed during constant-speed driving, the weight of the vehicle 1, and the power transmission efficiency in the vehicle 1. The amount of regenerated power obtained due to the vehicle 1 stopping at the traffic signal 3 is obtained based on, for example, the target vehicle speed during constant-speed driving, the weight of the vehicle 1, and the regeneration efficiency in the vehicle 1. The target vehicle speed during the above constant-speed driving is, for example, the legal speed in the above simulation.

[0050] Here, from the perspective of more accurately predicting the total power consumption of the vehicle 1 when the vehicle 1 travels on the driving route 2, in addition to the number of stops, it is preferable for the processing unit 32 to predict the total power consumption based on other information.

[0051] For example, in addition to the number of stops, the processing unit 32 may predict the total power consumption based on the stop time. Here, even when the vehicle 1 is stopped, power consumption occurs due to various auxiliary machines such as the air conditioner and audio device in the vehicle 1 being driven. Therefore, the longer the stop time at the traffic signal 3, the greater the power consumption. Thus, for example, the processing unit 32 predicts a larger value as the total power consumption when the vehicle 1 travels on the driving route 2, the longer the stop time.

[0052] Also, for example, in addition to the number of stops, the processing unit 32 may predict the total power consumption based on the continuous driving distance. Here, when the continuous driving distance is short, a situation may occur where the vehicle 1 cannot accelerate to the target vehicle speed after stopping at the traffic signal 3 until it stops at the traffic signal 3 next. The target vehicle speed is, for example, the legal speed in the above simulation. As a result, for example, the power consumption increases due to a shorter time available for constant-speed driving. Thus, for example, the processing unit 32 predicts that the shorter the continuous driving distance, the greater the power consumption in the section corresponding to the continuous driving distance, and predicts a larger value as the total power consumption when the vehicle 1 travels on the driving route 2.

[0053] In addition to the number of stops, the processing unit 32 may predict the total power consumption based on both the stop time and the continuous driving distance, or may predict the total power consumption based on either the stop time or the continuous driving distance in addition to the number of stops.

[0054] As described above, in the first example, the processing device 30 acquires pattern information, which is information indicating the operation pattern of the traffic signal 3 existing on the travel route 2 of the vehicle 1, and predicts the total consumption of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2 based on the pattern information. Thereby, since the total consumption can be predicted in consideration of the operation pattern of the traffic signal 3, the consumption of the energy source of the vehicle 1 can be accurately predicted. In particular, in the first example, the processing device 30 determines a recommended travel route on which the consumption of the energy source is suppressed based on the above total consumption. Thereby, the recommended travel route can be appropriately determined. Note that in the above example, the energy source is electric power.

[0055] FIG. 6 is a flowchart showing a second example of the processing flow performed by the processing device 30. The control flow shown in FIG. 6 is repeatedly executed, for example, at a preset time interval.

[0056] When the control flow shown in FIG. 6 is started, first, in step S301, the processing device 30 predicts the total consumption of the power of the battery 21 when traveling on the travel route 2. The travel route 2 in the second example of FIG. 6 is, for example, a route selected by the driver and to be guided by the navigation device 23.

[0057] In step S301, the control flow of FIG. 4 described above is executed.

[0058] Next to step S301, in step S302, the processing unit 32 of the processing device 30 predicts the travelable distance of the vehicle 1, and the control flow shown in FIG. 6 ends.

[0059] In step S302, for example, the processing unit 32 predicts the cruising range of the vehicle 1 based on the remaining capacity of the battery 21 and the total power consumption predicted in step S301. Specifically, the processing unit 32 subtracts the total power consumption predicted in step S301 from the remaining capacity of the battery 21. Then, the processing unit 32 multiplies the value thus obtained by the power consumption rate of the vehicle 1 to predict the distance that the vehicle 1 can travel after finishing traveling on the travel route 2. Further, the processing unit 32 predicts the sum of the distance that the vehicle 1 can travel after finishing traveling on the travel route 2 and the travel distance of the travel route 2 as the cruising range. Then, the processing unit 32 notifies the driver of the cruising range, for example, by causing the navigation device 23 to display the cruising range.

[0060] As described above, in the second example, similar to the first example described above, the processing device 30 acquires pattern information, which is information indicating the operation pattern of the traffic signal 3 existing on the travel route 2 of the vehicle 1, and predicts the total consumption of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2 based on the pattern information. Thereby, since the total consumption can be predicted taking into account the operation pattern of the traffic signal 3, the consumption of the energy source of the vehicle 1 can be predicted accurately. In particular, in the second example, the processing device 30 predicts the cruising range of the vehicle 1 based on the above total consumption. Thereby, the cruising range of the vehicle 1 can be predicted accurately. Note that in the above example, the energy source is electric power.

[0061] In the above, with reference to the flowchart of FIG. 3, the flowchart of FIG. 4, and the flowchart of FIG. 6, the first example and the second example have been described as examples of the processing performed by the processing device 30. However, the processing performed by the processing device 30 is not limited to the above examples.

[0062] For example, in the above, in the first example and the second example, an example was described in which the processing unit 32 predicts the total power consumption when the vehicle 1 travels on the travel route 2 based on the number of stops. However, the processing unit 32 may predict the total power consumption without specifying a value corresponding to the number of stops. For example, the processing unit 32 may predict the total power consumption by also calculating the power fluctuations of the battery 21 during the above-described simulation.

[0063] Also, for example, the processing device 30 may determine a recommended travel route based on the total consumption of the energy source when the vehicle 1 travels on a travel route as in the first example, and may predict the cruising range of the vehicle 1 based on the above total consumption as in the second example.

[0064] Also, for example, when the processing device 30 acquires information such as an accident, traffic control, or road closure on the travel route 2 from a computer of the traffic control center, the processing device 30 outputs the information to the navigation device 23, and the navigation device 23 may extract travel route candidates based on the information.

[0065] <Effect of the processing device> Subsequently, the effect of the processing device 30 according to the embodiment of the present invention will be described.

[0066] The processor 30a of the processing device 30 according to the present embodiment acquires pattern information, which is information indicating the operation pattern of at least one traffic signal 3 existing on the travel route 2 of the vehicle 1, and predicts the total consumption of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2 based on the pattern information. Thereby, since the above total consumption can be predicted in consideration of the operation pattern of the traffic signal 3, the consumption of the energy source of the vehicle 1 can be accurately predicted. In the above example, the energy source is electric power.

[0067] Also, it is preferable that the processor 30a of the processing device 30 according to the present embodiment executes a process including determining a recommended driving route on which the consumption of the energy source is suppressed based on the above total consumption amount. Thereby, since the recommended driving route can be determined after predicting the above total consumption amount in consideration of the operation pattern of the traffic signal 3, the recommended driving route can be appropriately determined.

[0068] Also, it is preferable that the processor 30a of the processing device 30 according to the present embodiment executes a process including predicting the cruising range of the vehicle 1 based on the above total consumption amount. Thereby, since the cruising range of the vehicle 1 can be predicted after predicting the above total consumption amount in consideration of the operation pattern of the traffic signal 3, the cruising range of the vehicle 1 can be accurately predicted.

[0069] Also, it is preferable that the processor 30a of the processing device 30 according to the present embodiment predicts the number of stops, which is the number of times the vehicle 1 stops at the traffic signal 3, based on the pattern information, and predicts the above total consumption amount based on the number of stops. Thereby, it is appropriately realized to accurately predict the total consumption amount of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2.

[0070] Also, it is preferable that the processor 30a of the processing device 30 according to the present embodiment predicts the stop time, which is the time when the vehicle 1 stops at the traffic signal 3, based on the pattern information, and predicts the above total consumption amount based on the stop time in addition to the number of stops. Thereby, the total consumption amount of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2 can be predicted more accurately.

[0071] Further, the processor 30a of the processing device 30 according to the present embodiment predicts a continuous driving distance that the vehicle 1 continuously drives until it stops at the traffic signal 3 after stopping at the traffic signal 3, based on the pattern information, and in addition to the number of stops, predicts the total consumption amount based on the continuous driving distance. It is preferable to execute the process including this. Thereby, the total consumption amount of the energy source of the vehicle 1 when the vehicle 1 travels on the travel route 2 can be predicted with higher accuracy.

[0072] As described above, the preferred embodiments of the present invention have been described with reference to the accompanying drawings. However, it goes without saying that the present invention is not limited to the above-described embodiments, and various modifications or corrections within the scope described in the claims also belong to the technical scope of the present invention.

[0073] For example, the processes described using flowcharts in this specification do not necessarily have to be executed in the order shown in the flowcharts. Also, additional processing steps may be adopted, and some processing steps may be omitted.

Explanation of Reference Numerals

[0074] 1 Vehicle 2 Travel Route 3 Traffic Signal 11 Communication Device 21 Battery 22 Driving Motor 23 Navigation Device 30 Processing Device 30a Processor 30b Memory 31 Acquisition Unit 32 Processing Unit

Claims

1. One or more processors, One or more memories connected to the processor, having, The processor is obtaining pattern information, which is information indicating an operation pattern of at least one traffic signal existing on a driving route of the vehicle, predicting the number of stops, which is the number of times the vehicle stops at the traffic signal, based on the pattern information, and predicting the total consumption of the energy source of the vehicle when the vehicle travels on the driving route based on the number of stops, executing a process including a processing device.

2. The processor executes a process including determining a recommended driving route on which consumption of the energy source is suppressed based on the total consumption. The processing device according to claim 1.

3. The processor executes a process including predicting a cruising range of the vehicle based on the total consumption. The processing device according to claim 1.

4. The processor is predicting a stop time, which is a time when the vehicle stops at the traffic signal, based on the pattern information, predicting the total consumption based on the stop time in addition to the number of stops, and executing a process including The processing device according to any one of claims 1 to 3.

5. The processor is predicting a continuous driving distance that the vehicle continuously travels until the next stop at the traffic signal after stopping at the traffic signal based on the pattern information, predicting the total consumption based on the continuous driving distance in addition to the number of stops, executing a process including the processing device according to any one of claims 1 to 3.

6. The vehicle includes, as the energy source, a battery that stores electric power supplied to a traveling motor, the processor subtracts the amount of regenerated electric power obtained due to the vehicle stopping from the amount of consumed electric power generated due to the vehicle accelerating after stopping, and based on the amount of consumed electric power generated due to stopping once at the traffic signal and the number of stops, executes a process including predicting the total consumption amount. the processing device according to any one of claims 1 to 3.

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