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

Processing equipment

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

[0002] A vehicle runs by consuming an energy source installed in the vehicle. For example, in an electric vehicle, power stored in a battery is used as the energy source. To assist the driver of the vehicle, it is important to predict the consumption of the vehicle's energy source. For example, Patent Literature 1 discloses a technology for predicting the vehicle's cruising range by predicting the consumption of the vehicle's energy source.

[0003] Japanese Patent Application Laid-Open No. 2012-220415

[0004] As described above, various techniques for predicting the consumption of a vehicle's energy source have been proposed to assist the driver of the vehicle. However, there is a need for further techniques for predicting the consumption of a vehicle's energy source with higher accuracy.

[0005] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide a processing device that can accurately predict the consumption of a vehicle's energy source.

[0006] In order to solve the above problem, a processing device according to one embodiment of the present invention has one or more processors and one or more memories connected to the processors, and the processor performs processing including: acquiring pattern information that is information indicating the operation pattern of at least one traffic light present on a vehicle's driving route; and predicting, based on the pattern information, the total consumption of the vehicle's energy sources when the vehicle travels the driving route.

[0007] According to the present invention, it is possible to accurately predict the consumption of a vehicle's energy source.

[0008] FIG. 1 is a schematic diagram showing a general configuration of a vehicle according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of the functional configuration of a processing device according to an embodiment of the present invention. FIG. 3 is a flowchart showing a first example of a processing flow performed by the processing device according to an embodiment of the present invention. FIG. 4 is a flowchart showing an example of a processing flow for predicting total power consumption performed by the processing device according to an embodiment of the present invention. FIG. 5 is a diagram showing a schematic diagram of a travel route of a vehicle according to an embodiment of the present invention. FIG. 6 is a flowchart showing a second example of a processing flow performed by the processing device according to an embodiment of the present invention.

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

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

[0011] 1 is a schematic diagram showing the general configuration of a vehicle 1. In the following, an example will be described in which the vehicle 1 is an electric vehicle equipped with a battery 21, and travels using the power stored in the battery 21 as an energy source. That is, in the following example, the travel 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 run using fuel supplied to the engine as an energy source. In this case, the running distance per unit capacity of the energy source is the fuel efficiency.

[0013] As shown in FIG. 1 , the vehicle 1 includes a communication device 11 , a battery 21 , a traction 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 is capable of charging and discharging power. The battery 21 may be, for example, a lithium-ion battery, a lithium-ion polymer battery, a nickel-metal hydride battery, a nickel-cadmium battery, or a lead-acid battery, but other batteries may also be used. The battery 21 stores the power supplied to the traction motor 22.

[0016] The traction motor 22 outputs power to be transmitted to the wheels of the vehicle 1, and is configured, for example, as a three-phase AC motor. The traction motor 22 is driven using power from the battery 21 to output power. The traction motor 22 is also capable of generating power using the kinetic energy of the wheels by being driven regeneratively when the vehicle 1 decelerates. In this case, the power generated by the traction motor 22 is supplied to the battery 21. As a result, the battery 21 is charged with the power generated by the traction motor 22.

[0017] The navigation device 23 is a device that provides guidance on a driving route from the current location of the vehicle 1 to a 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 candidate driving routes and proposes the determined recommended driving route to the user. In this specification, the recommended driving route is a driving route that reduces consumption of energy sources. The driver refers to the recommended driving route and selects a driving route that the navigation device 23 will guide the user on from among the plurality of candidate driving routes.

[0018] Specifically, the navigation device 23 has a function of visually displaying information and displays various information related to route guidance. Examples of 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 time required to reach the destination. The navigation device 23 can obtain information indicating the current location of the vehicle 1 based on signals transmitted from GPS (Global Positioning System) satellites.

[0019] The processing device 30 has one or more processors 30a and one or more memories 30b connected to the processors 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 calculation parameters used by the CPU. The RAM is a storage element that temporarily stores data such as variables and parameters used in processing 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 using, for example, CAN (Controller Area Network) communication.

[0021] 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 has 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 may be executed by the processor 30a. In detail, the various processes are executed by the processor 30a executing programs stored in the memory 30b.

[0022] The acquisition unit 31 acquires various types of information and outputs the information 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, acquisition of information may include extraction or generation (e.g., 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] The functions of the processing device 30 according to the present embodiment may be divided among multiple devices, or multiple functions may be realized by a single device. When the functions of the processing device 30 are divided among multiple devices, the multiple devices may be connected to each other via a communication bus such as a CAN.

[0025] <Operation of Processing Apparatus> Next, the operation of the processing apparatus 30 according to the embodiment of the present invention will be described with reference to FIGS.

[0026] In this embodiment, the processing device 30 acquires pattern information that indicates the operation patterns of traffic lights present on the driving route of the vehicle 1, and predicts the total consumption of the energy sources of the vehicle 1 when the vehicle 1 travels along the driving route based on the pattern information. As a result, as will be described later, it is possible to accurately predict the consumption of the energy sources of the vehicle 1. In the following example, the energy source is electricity.

[0027] The traffic light operation pattern indicated by the pattern information is, for example, a pattern of when the traffic light changes color and to which color. Examples of pattern information include information that chronologically arranges information indicating the color of the traffic light at each time, or information indicating the cycle at which the traffic light changes color. In the following, an example is described in which the processing device 30 acquires pattern information indicating the operation patterns of multiple traffic lights that exist on the travel route of the vehicle 1. However, it is sufficient for the processing device 30 to acquire pattern information that indicates the operation pattern of at least one traffic light that exists on the travel route of the vehicle 1.

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

[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 executed repeatedly at preset time intervals, for example.

[0030] 3 starts, first, in step S101, the processing device 30 predicts the total amount of power consumption for each candidate travel route. Specifically, the processing device 30 predicts the total amount of power consumption of the battery 21 when traveling along each candidate travel route. The candidate travel routes are extracted by the navigation device 23, for example, based on the current location of the vehicle 1 and a destination desired by the user. Details of the processing of step S101 will be described later with reference to FIG. 4.

[0031] Following step S101, in step S102, the processing unit 32 of the processing device 30 determines a recommended driving route, and the control flow shown in FIG. 3 ends. As described above, the recommended driving route is a driving route that reduces power consumption. That is, in step S101, the processing unit 32 determines the driving route candidate that has the smallest predicted total power consumption as the recommended driving route. Then, the processing unit 32 notifies the driver of the recommended driving route by, for example, displaying the recommended driving route on the navigation device 23.

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

[0033] Specifically, the control flow shown in Fig. 4 is a control flow for predicting the total amount of power consumption for one travel route. Therefore, in step S101 in Fig. 3, the control flow shown in Fig. 4 is performed sequentially for all candidate travel routes.

[0034] FIG. 5 is a diagram schematically illustrating a driving route 2 of a vehicle 1. For example, if the driving route 2 shown in FIG. 5 is extracted as a candidate driving route, the total amount of power consumed by the battery 21 when the vehicle 1 travels along the driving route 2 is predicted in the control flow shown in FIG. 4. As shown in FIG. 5, there are multiple traffic lights 3 along the driving route 2. There are two types of traffic lights 3: centralized control type and decentralized control type. Centralized control type traffic lights are installed in specific areas with relatively high traffic volume and are controlled by a computer at a traffic control center. Decentralized control type traffic lights are installed in areas with relatively low traffic volume and are controlled independently without being connected to a computer at a traffic control center.

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

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

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

[0038] Furthermore, if the traffic light 3 from which pattern information is to be acquired is a 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 advance in the navigation device 23.

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

[0040] In step S202, the processing unit 32 performs a simulation, for example, assuming that each traffic light 3 on the travel route 2 operates according to the operation pattern indicated by the pattern information, and that the vehicle 1 accelerates and travels up to the legal speed limit. This simulation is performed based on the number, positions, and pattern information of traffic lights 3 on the travel route 2, and the legal speed limit for the travel route 2. In this simulation, the vehicle 1 stops at red traffic lights 3 and travels without stopping at any other traffic lights. In this simulation, the vehicle 1 accelerates when its vehicle speed is below the legal speed limit, and maintains its speed when its vehicle speed has reached the legal speed limit. In this simulation, the vehicle 1 accelerates at a predetermined acceleration rate. The acceleration is set, for example, using an average value of the vehicle 1's past acceleration data. In this simulation, the vehicle 1 decelerates at a predetermined speed change rate before the red traffic light 3 so that it can stop at the red traffic light 3. The speed change rate is set, for example, using an average value of the vehicle 1's past deceleration data.

[0041] Based on the results of the simulation, the processing unit 32 can predict the behavior of the vehicle 1 when the vehicle 1 travels along the travel route 2. Then, in step S202, the processing unit 32 predicts the number of stops based on the results of the simulation. Specifically, in the above-described simulation, the processing unit 32 can identify the speed of the vehicle 1 at each time point, and therefore the position of the vehicle 1 at each time point. The processing unit 32 can also identify the color of each traffic light 3 at each time point. Therefore, the processing unit 32 can identify the color of the traffic light 3 when the vehicle 1 reaches the installation position of the traffic light 3. Therefore, the processing unit 32 can identify at which traffic light 3 the vehicle 1 will stop. In this way, for example, the processing unit 32 can predict the number of stops. In detail, the processing unit 32 can also predict at which traffic light 3 the vehicle 1 will stop.

[0042] Following step S202, in step S203, the processing unit 32 predicts, based on the pattern information, the stop time during which the vehicle 1 will stop at the traffic light 3. Note that, when it is predicted that the vehicle 1 will stop at multiple traffic lights 3, the processing unit 32 predicts the stop time for each of the multiple traffic lights 3.

[0043] In step S203, the processing unit 32 predicts the stop time based on, for example, the results of the simulation described above. Specifically, in the simulation described above, the processing unit 32 can identify the speed of the vehicle 1 at each time point, and therefore the position of the vehicle 1 at each time point. The processing unit 32 can also identify the color of each traffic light 3 at each time point. Therefore, when the vehicle 1 arrives at a red traffic light 3 and stops there, the processing unit 32 can identify the time from when the vehicle 1 stops to when the color of the traffic light 3 changes from red to green and the vehicle 1 starts moving. In this way, for example, the processing unit 32 can predict the stop time. In detail, the processing unit 32 can also predict at which traffic light 3 the vehicle 1 will stop and for how long.

[0044] Following step S203, in step S204, the processing unit 32 predicts, based on the pattern information, a continuing driving distance that the vehicle 1 will continue to travel after stopping at a traffic light 3 until the next time the vehicle stops at the traffic light 3. When the vehicle 1 is predicted to stop at multiple traffic lights 3, the distance between traffic lights 3 that are arranged consecutively among the multiple traffic lights 3 (specifically, the distance along the road) corresponds to the continuing driving distance.

[0045] In step S204, the processing unit 32 predicts the continued driving distance based on, for example, the results of the simulation described above.

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

[0047] In step S205, the processing unit 32 predicts the total amount of power consumption based on, for example, the number of stops. Here, when the vehicle 1 stops at a traffic light 3, the vehicle 1 needs to accelerate unnecessarily due to the stop. Therefore, when the vehicle 1 stops at a traffic light 3, an energy loss occurs due to the vehicle 1 accelerating and decelerating unnecessarily compared to when the vehicle 1 passes through the traffic light 3 without stopping. In this way, power consumption occurs due to the vehicle 1 stopping at the traffic light 3. The processing unit 32 can predict the total amount of power consumption by multiplying the number of stops by the amount of power consumption caused by the vehicle 1 stopping once at the traffic light 3.

[0048] For example, the amount of power consumption resulting from vehicle 1 stopping once at traffic light 3 is stored in advance in a storage element of processing device 30. Then, processing unit 32 multiplies the amount of power consumption resulting from vehicle 1 stopping once at traffic light 3 by the number of stops. Furthermore, processing unit 32 adds the value obtained in this way to the total amount of power consumption expected if vehicle 1 travels along travel route 2 without stopping at any traffic light 3. Then, processing unit 32 predicts the finally obtained value as the total amount of power consumption of battery 21 if vehicle 1 travels along travel route 2.

[0049] The amount of power consumed due to vehicle 1 stopping once at traffic light 3 can be calculated, for example, by subtracting the amount of power regenerated due to vehicle 1 stopping at traffic light 3 from the amount of power consumed due to vehicle 1 accelerating after stopping. The amount of power consumed due to vehicle 1 accelerating after stopping can be calculated based on, for example, the target vehicle speed during constant speed travel, the weight of vehicle 1, and the power transmission efficiency of vehicle 1. The amount of power regenerated due to vehicle 1 stopping at traffic light 3 can be calculated based on, for example, the target vehicle speed during constant speed travel, the weight of vehicle 1, and the regeneration efficiency of vehicle 1. In the above simulation, the target vehicle speed during constant speed travel is, for example, the legal speed.

[0050] Here, from the viewpoint of more accurately predicting the total amount of electricity consumed by vehicle 1 when vehicle 1 travels along travel route 2, it is preferable that processing unit 32 predicts the total amount of electricity consumed based on other information in addition to the number of stops.

[0051] For example, the processing unit 32 may predict the total amount of power consumption based on the stop duration in addition to the number of stops. Here, even while the vehicle 1 is stopped, power consumption occurs due to the operation of various auxiliary devices inside the vehicle 1, such as the air conditioning system and audio system. Therefore, the longer the stop duration at the traffic light 3, the greater the amount of power consumption. Therefore, for example, the longer the stop duration, the larger the predicted value of the total amount of power consumption when the vehicle 1 travels along the travel route 2.

[0052] Furthermore, for example, the processing unit 32 may predict the total amount of power consumption based on the continuous driving distance in addition to the number of stops. Here, if the continuous driving distance is short, a situation may arise in which the vehicle 1 cannot accelerate to the target vehicle speed after stopping at a traffic light 3 until the next time the vehicle stops at the traffic light 3. Note that the target vehicle speed is, for example, the legal speed in the above simulation. This increases the amount of power consumption, for example, due to a shorter time available for constant speed driving. Therefore, the processing unit 32 predicts that the shorter the continuous driving distance, the greater the amount of power consumption in the section corresponding to the continuous driving distance, and predicts a large value as the total amount of power consumption when the vehicle 1 travels the driving route 2.

[0053] In addition, the processing unit 32 may predict the total amount of electricity consumption based on both the number of stops and the stopping time and the continued driving distance, or may predict the total amount of electricity consumption based on the number of stops and either the stopping time or the continued driving distance.

[0054] As described above, in the first example, the processing device 30 acquires pattern information, which is information indicating the operation patterns of traffic lights 3 present on the driving route 2 of the vehicle 1, and predicts the total consumption of the energy sources of the vehicle 1 when the vehicle 1 travels the driving route 2 based on the pattern information. This makes it possible to predict the total consumption taking into account the operation patterns of the traffic lights 3, thereby making it possible to accurately predict the consumption of the energy sources of the vehicle 1. In particular, in the first example, the processing device 30 determines a recommended driving route that reduces the consumption of the energy sources based on the total consumption. This makes it possible to appropriately determine the recommended driving route. Note that in the above example, the energy source is electricity.

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

[0056] 6 is started, first, in step S301, the processing device 30 predicts the total amount of power consumption of the battery 21 when traveling along 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 shown in FIG. 4 is executed.

[0058] After step S301, in step S302, the processing unit 32 of the processing device 30 predicts the cruising range 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 amount of power consumption predicted in step S301. Specifically, the processing unit 32 subtracts the total amount of power consumption predicted in step S301 from the remaining capacity of the battery 21. Then, the processing unit 32 multiplies the value obtained in this way by the power consumption rate of the vehicle 1 to predict the distance that the vehicle 1 can travel after completing traveling the traveling route 2. Furthermore, the processing unit 32 predicts the total value of the distance that the vehicle 1 can travel after completing traveling the traveling route 2 and the traveling distance of the traveling route 2 as the cruising range. Then, the processing unit 32 notifies the driver of the cruising range by, for example, displaying the cruising range on the navigation device 23.

[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 patterns of traffic lights 3 present on the driving route 2 of the vehicle 1, and predicts the total consumption of the energy sources of the vehicle 1 when the vehicle 1 travels the driving route 2, based on the pattern information. This makes it possible to predict the total consumption taking into account the operation patterns of the traffic lights 3, and therefore to accurately predict the consumption of the energy sources of the vehicle 1. In particular, in the second example, the processing device 30 predicts the cruising range of the vehicle 1 based on the total consumption. This makes it possible to accurately predict the cruising range of the vehicle 1. Note that in the above example, the energy source is electricity.

[0061] The first and second examples have been described above with reference to the flowcharts of Figures 3, 4, and 6 as examples of 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 first and second examples described above, the processing unit 32 predicts the total amount of power consumption when the vehicle 1 travels the travel route 2 based on the number of stops. However, the processing unit 32 may predict the total amount of power consumption without specifying a value corresponding to the number of stops. For example, the processing unit 32 may predict the total amount of power consumption by also calculating fluctuations in the power of the battery 21 during the above-mentioned simulation.

[0063] Also, for example, the processing device 30 may determine a recommended driving route based on the total consumption of energy sources when the vehicle 1 travels along the driving 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] Furthermore, for example, when the processing device 30 acquires information on accidents, traffic control, road closures, etc. on the driving route 2 from a computer at a traffic control center, the processing device 30 outputs the information to the navigation device 23, and the navigation device 23 may extract candidate driving routes based on the information.

[0065] <Effects of the Processing Apparatus> Next, the effects of the processing apparatus 30 according to the embodiment of the present invention will be described.

[0066] The processor 30a of the processing device 30 according to this embodiment executes processing including acquiring pattern information that is information indicating the operation pattern of at least one traffic light 3 present on the travel route 2 of the vehicle 1, and predicting, based on the pattern information, the total consumption of the energy source of the vehicle 1 when the vehicle 1 travels the travel route 2. This makes it possible to predict the total consumption taking into account the operation pattern of the traffic light 3, thereby enabling accurate prediction of the consumption of the energy source of the vehicle 1. In the above example, the energy source is electricity.

[0067] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment executes processing that includes determining a recommended driving route that reduces consumption of energy sources based on the total consumption amount. This allows the recommended driving route to be determined after predicting the total consumption amount in consideration of the operation patterns of the traffic lights 3, making it possible to appropriately determine the recommended driving route.

[0068] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment executes processing that includes predicting the cruising range of the vehicle 1 based on the total consumption amount. This allows the cruising range of the vehicle 1 to be predicted after predicting the total consumption amount taking into account the operation pattern of the traffic lights 3, thereby enabling the cruising range of the vehicle 1 to be predicted with high accuracy.

[0069] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment executes processing including predicting the number of stops, which is the number of times the vehicle 1 will stop at traffic lights 3, based on the pattern information, and predicting the total consumption amount based on the number of stops. This appropriately realizes accurate prediction of the total consumption amount of the energy sources of the vehicle 1 when the vehicle 1 travels along the travel route 2.

[0070] Furthermore, the processor 30a of the processing device 30 according to this embodiment preferably executes processing including predicting the stop time, which is the time that the vehicle 1 will stop at the traffic light 3, based on the pattern information, and predicting the total consumption amount based on the stop time in addition to the number of stops. This makes it possible to more accurately predict the total consumption amount of the energy sources of the vehicle 1 when the vehicle 1 travels the travel route 2.

[0071] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment executes processing including predicting, based on the pattern information, the distance that the vehicle 1 will continue to travel after stopping at a traffic light 3 until the next time the vehicle stops at the traffic light 3, and predicting the total consumption amount based on the number of stops and the distance that the vehicle 1 will continue to travel. This makes it possible to more accurately predict the total consumption amount of the energy sources of the vehicle 1 when the vehicle 1 travels the travel route 2.

[0072] The above describes a preferred embodiment of the present invention with reference to the accompanying drawings. However, it goes without saying that the present invention is not limited to the above-described embodiment, and various modified or altered examples within the scope of the claims also fall within the technical scope of the present invention.

[0073] For example, the processes described herein using flowcharts do not necessarily have to be performed in the order shown in the flowcharts, and additional process steps may be employed or some process steps may be omitted.

[0074] REFERENCE SIGNS LIST 1 vehicle 2 driving route 3 traffic light 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 coupled to the processor; having The processor, Obtaining pattern information which is information indicating an operation pattern of at least one traffic light present on a travel route of a vehicle; predicting a stop count, which is a number of times the vehicle will stop at the traffic light, based on the pattern information; predicting a total consumption amount of an energy source of the vehicle when the vehicle travels the travel route based on the number of stops; Perform a process that includes Processing unit.

2. The processor executes a process including determining a recommended driving route that reduces consumption of the energy source based on the total consumption. The processing device of claim 1 .

3. the processor performs processes including predicting a range of the vehicle based on the total consumption. The processing device of claim 1 .

4. The processor, predicting a stop time, which is a time for the vehicle to stop at the traffic light, based on the pattern information; predicting the total consumption based on the number of stops as well as the duration of the stops; Perform a process that includes The processing device according to any one of claims 1 to 3.

5. The processor, predicting a distance that the vehicle will continue to travel after stopping at the traffic light until the vehicle next stops at the traffic light based on the pattern information; predicting the total consumption based on the number of stops as well as the driving distance; Perform a process that includes The processing device according to any one of claims 1 to 3.

6. The vehicle includes a battery as the energy source for storing power supplied to a driving motor, The processor executes a process including predicting the total consumption amount based on the amount of power consumption caused by stopping the vehicle once at the traffic light, the amount of power consumption being obtained by subtracting the amount of power regeneration obtained by stopping the vehicle from the amount of power consumption caused by accelerating the vehicle after stopping, and the number of stops. The processing device according to any one of claims 1 to 3.