Processing device

Abstract

This processing device comprises one or more processors and one or more memories connected to the processors, wherein the one or more processors perform processing that includes identifying consumption characteristics of the vehicle regarding an energy source on the basis of a target vehicle speed according to the environment in which the vehicle travels and the actual vehicle speed of the vehicle.

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] In order to assist a driver of a vehicle, it is desirable to predict the consumption characteristics of the vehicle's energy source with higher accuracy.

[0005] SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a processing device that can predict the consumption characteristics of a vehicle's energy source with higher accuracy.

[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 processors perform processing including identifying the consumption characteristics of the vehicle's energy source based on a target vehicle speed according to the vehicle's driving environment and the actual vehicle speed of the vehicle.

[0007] According to the present invention, it is possible to more accurately predict the consumption characteristics 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 the flow of processing performed by the processing device according to an embodiment of the present invention. FIG. 4 is a diagram for explaining the processing for obtaining first operation characteristic information and the processing for specifying first consumption characteristics performed by the processing device according to an embodiment of the present invention. FIG. 5 is a flowchart showing a second example of the flow of processing 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 consumption rate.

[0013] As shown in FIG. 1, the vehicle 1 includes a vehicle speed sensor 11 , an ambient environment sensor 12 , a yaw rate sensor 13 , a battery 21 , a traction motor 22 , a navigation device 23 , and a processing device 30 .

[0014] The vehicle speed sensor 11 detects the speed of the vehicle 1 .

[0015] The surrounding environment sensor 12 detects surrounding environment information related to the environment surrounding the vehicle 1. Examples of the surrounding environment sensor 12 include a camera, a radar, and an ultrasonic sensor. Specifically, the surrounding environment sensor 12 is provided at the front of the vehicle 1 and detects surrounding environment information ahead of the vehicle 1. For example, the surrounding environment sensor 12 detects information related to the behavior of a preceding vehicle traveling ahead of the vehicle 1 as the surrounding environment information. Examples of the information related to the behavior include speed and acceleration. Furthermore, for example, the surrounding environment sensor 12 detects information related to the positional relationship between the road on which the vehicle 1 is traveling and the vehicle 1 as the surrounding environment information. Examples of the information related to the positional relationship include information indicating which lane the vehicle 1 is traveling in, or information indicating the position of the vehicle 1 within the lane.

[0016] The yaw rate sensor 13 detects the yaw rate occurring in the vehicle 1 .

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] The processing device 30 communicates with each device in the vehicle 1, such as the vehicle speed sensor 11, the ambient environment sensor 12, the yaw rate sensor 13, 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.

[0023] 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.

[0024] The acquisition unit 31 acquires various types of information and outputs it to the processing unit 32. For example, the acquisition unit 31 acquires information from the vehicle speed sensor 11, the surrounding environment sensor 12, the yaw rate sensor 13, and the navigation device 23. In this specification, acquisition of information may include extraction or generation of information. Generation of information may be, for example, calculation.

[0025] 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.

[0026] 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.

[0027] <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.

[0028] In this embodiment, the processing device 30 identifies the consumption characteristics of the energy source of the vehicle 1 based on a target vehicle speed according to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1. Specifically, operation characteristic information, which is information indicating the driving operation characteristics specific to the driver of the vehicle 1, is acquired based on the target vehicle speed according to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1, and the consumption characteristics of the energy source of the vehicle 1 are identified based on the operation characteristic information. This makes it possible to more accurately predict the consumption characteristics of the energy source of the vehicle 1, as will be described later. In the following example, the energy source is electricity.

[0029] The driving operation characteristics are characteristics of the driver's driving operation when driving the vehicle 1, for example, driving habits specific to the driver. Specifically, the behavior of the vehicle 1, such as the vehicle speed, reflects the driver's driving operation. Therefore, the processing device 30 collects information on the behavior of the vehicle 1 while it is traveling, and acquires operation characteristic information based on information on the behavior of the vehicle 1 collected in the past. The energy source consumption characteristics of the vehicle 1 are characteristics of the consumption of the energy source resulting from the driver's specific driving operation.

[0030] Below, a first example and a second example are described in order as examples of processing performed by the processing device 30. Note that below, an example is described in which first operation characteristic information, second operation characteristic information, and third operation characteristic information are acquired as operation characteristic information, and first consumption characteristic, second consumption characteristic, and third consumption characteristic are identified as consumption characteristics. However, as will be described later, the acquired operation characteristic information and the identified consumption characteristics are not limited to the following examples.

[0031] 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.

[0032] 3 starts, first, in step S101, the acquisition unit 31 acquires operation characteristic information. After step S101, in step S102, the processing unit 32 identifies consumption characteristics based on the operation characteristic information.

[0033] First, an example will be described in which first operation characteristic information is acquired in step S101 and first consumption characteristics are identified in step S102. The first operation characteristic information is operation characteristic information that indicates the tendency of acceleration and deceleration of vehicle 1 when vehicle 1 is following a preceding vehicle. Following a preceding vehicle means that the driver of vehicle 1 is following a preceding vehicle.

[0034] For example, in step S101, the acquisition unit 31 acquires, as first operating characteristic information, information indicating the extent to which the vehicle 1 is accelerating or decelerating relative to an ideal vehicle speed (target vehicle speed) at which excessive acceleration or deceleration does not occur when the vehicle 1 is following the vehicle.

[0035] The acquisition unit 31 determines whether the vehicle 1 is following a vehicle ahead based on, for example, the detection result of the surrounding environment sensor 12. For example, when the surrounding environment sensor 12 detects a leading vehicle and the inter-vehicle distance between the vehicle 1 and the leading vehicle detected by the surrounding environment sensor 12 is less than a predetermined distance, the acquisition unit 31 can determine that the vehicle 1 is following a vehicle ahead. The predetermined distance is, for example, 50 m.

[0036] In determining whether vehicle 1 is following another vehicle, the vehicle speed of vehicle 1 may be further taken into consideration. Specifically, a condition that the vehicle speed is equal to or lower than a predetermined speed, for example, 40 km / h or lower, may be added to determine whether vehicle 1 is following another vehicle in a traffic jam.

[0037] FIG. 4 is a diagram illustrating the process of acquiring first operation characteristic information and the process of identifying the first consumption characteristic, which will be described later, performed by the processing device 30. The horizontal axis T in FIG. 4 represents time, and the vertical axis V in FIG. 4 represents vehicle speed. For example, the acquisition unit 31 first estimates a transition of an ideal vehicle speed V0, which is free of excessive acceleration or deceleration, based on the vehicle speed of the preceding vehicle detected by the ambient environment sensor 12. For example, the vehicle speed of the preceding vehicle may be excessively accelerated or decelerated due to the driving operation of the driver of the preceding vehicle. Therefore, the acquisition unit 31 can estimate a transition of the ideal vehicle speed V0, which is free of excessive acceleration or deceleration, by processing the vehicle speed of the preceding vehicle using a filter such as a low-pass filter.

[0038] The acquisition unit 31 acquires, as the first operation characteristic information, a comparison result between the vehicle speed V1, which is the actual vehicle speed of the vehicle 1 detected by the vehicle speed sensor 11 during the following travel of the vehicle 1, and the ideal vehicle speed V0 during the following travel of the vehicle 1. For example, in region R1 in Fig. 4, the vehicle speed V1 of the vehicle 1 is higher than the ideal vehicle speed V0, and the vehicle 1 is accelerating excessively. On the other hand, in region R2 in Fig. 4, the vehicle speed V1 of the vehicle 1 is lower than the ideal vehicle speed V0, and the vehicle 1 is decelerating excessively. The acquisition unit 31 acquires, as the first operation characteristic information, information such as the occurrence frequency of region R1, the amount of deviation between the vehicle speed V1 and the vehicle speed V0 in region R1, the occurrence frequency of region R2, and the amount of deviation between the vehicle speed V1 and the vehicle speed V0 in region R2.

[0039] When the first operation characteristic information is acquired in step S101, the processing unit 32 identifies a first consumption characteristic based on the first operation characteristic information in step S102. The first consumption characteristic is a power consumption characteristic during follow-up driving. The processing unit 32 stores the acquired first operation characteristic information in the memory 30b of the processing device 30, for example, and identifies the first consumption characteristic using the stored first operation characteristic information. This makes it possible to identify the first consumption characteristic using information stored in advance.

[0040] For example, the processing unit 32 identifies the power consumption characteristics resulting from excessive acceleration and deceleration of the vehicle 1 during follow-up driving as the first consumption characteristics. For example, in the example of Fig. 4, the vehicle 1 decelerates unnecessarily in region R2, and accelerates unnecessarily in region R1. In this way, power consumption occurs due to excessive acceleration and deceleration of the vehicle 1 during follow-up driving.

[0041] For example, the processing unit 32 estimates the amount of power consumption caused by excessive acceleration based on the deviation between the vehicle speed V1 and the vehicle speed V0 in each region R1. Note that the processing unit 32 may also take the weight of the vehicle 1 into account when estimating the amount of power consumption. The processing unit 32 then estimates the amount of power regeneration resulting from excessive deceleration based on the deviation between the vehicle speed V1 and the vehicle speed V0 in each region R2. The processing unit 32 then estimates the amount of power consumption caused by excessive acceleration and deceleration during follow-up driving by subtracting the amount of power regeneration resulting from excessive deceleration from the amount of power consumption caused by excessive acceleration. For example, based on the information thus obtained, the processing unit 32 identifies, as the first consumption characteristic, information indicating the amount of power consumption per unit driving distance during follow-up driving.

[0042] In this way, the processing unit 32 determines the first consumption characteristic based on the target vehicle speed according to the driving environment (in the above example, the vehicle speed V0 calculated from the vehicle speed of the preceding vehicle) and the actual vehicle speed V1 of the vehicle 1.

[0043] Next, an example will be described in which second operation characteristic information is acquired in step S101 and second consumption characteristics are identified in step S102. The second operation characteristic information is operation characteristic information that indicates the tendency of acceleration / deceleration of the vehicle 1 when the vehicle 1 is turning.

[0044] For example, in step S101, the acquisition unit 31 acquires, as second operating characteristic information, information indicating the extent to which the vehicle 1 is decelerating relative to the ideal vehicle speed (target vehicle speed) corresponding to the radius of curvature of the road when the vehicle 1 is turning.

[0045] The acquisition unit 31 determines whether the vehicle 1 is turning, for example, based on the detection result of the yaw rate sensor 13. For example, when the yaw rate detected by the yaw rate sensor 13 is relatively large, the acquisition unit 31 can determine that the vehicle 1 is turning.

[0046] For example, the acquisition unit 31 first estimates the radius of curvature of the road based on the yaw rate of the vehicle 1 detected by the yaw rate sensor 13 when the vehicle 1 is turning and the vehicle speed of the vehicle 1 detected by the vehicle speed sensor 11. The acquisition unit 31 may also estimate the radius of curvature of the road based on ambient environment information acquired by the ambient environment sensor 12. The acquisition unit 31 then identifies an ideal vehicle speed V0 corresponding to the estimated radius of curvature of the road. An example of the ideal vehicle speed V0 is a vehicle speed obtained by multiplying an upper limit vehicle speed within a range in which tire slip does not occur during turning by a predetermined safety factor. Here, the upper limit vehicle speed is a value that is preset for each radius of curvature of the road, and the predetermined safety factor is, for example, 80%. When identifying the upper limit vehicle speed, the acquisition unit 31 may also take into account the weight of the vehicle 1, the friction characteristics of the tires, or the like. The acquisition unit 31 then acquires, as second operation characteristic information, a comparison result between the vehicle speed V1 of the vehicle 1 detected by the vehicle speed sensor 11 when the vehicle 1 is turning and the ideal vehicle speed V0 when turning. For example, the acquisition unit 31 acquires, for each of a plurality of turns, the amount of deviation between the vehicle speed V1 and the vehicle speed V0 when turning, as the second operation characteristic information.

[0047] If the second operation characteristic information is acquired in step S101, the processing unit 32 determines the second consumption characteristic based on the second operation characteristic information in step S102. The second consumption characteristic is the power consumption characteristic during cornering. The processing unit 32 stores the acquired second operation characteristic information in the memory 30b of the processing device 30, for example, and determines the second consumption characteristic using the stored second operation characteristic information. This allows the second consumption characteristic to be determined using information stored in advance.

[0048] For example, the processing unit 32 identifies the power consumption characteristic resulting from excessive acceleration and deceleration of the vehicle 1 when turning as the second consumption characteristic. When turning, if the vehicle 1 decelerates unnecessarily relative to the ideal vehicle speed corresponding to the radius of curvature of the road, it becomes necessary to accelerate unnecessarily. In this way, power consumption occurs due to excessive acceleration and deceleration of the vehicle 1 when turning.

[0049] For example, the processing unit 32 estimates the amount of power consumption caused by excessive acceleration / deceleration during turning based on the deviation between the vehicle speed V1 of the vehicle 1 during turning and the ideal vehicle speed V0. Specifically, the processing unit 32 estimates a larger value as the amount of power consumption caused by excessive acceleration / deceleration during turning, the greater the deviation between the vehicle speed V1 of the vehicle 1 during turning and the ideal vehicle speed V0. Then, based on the information obtained in this manner, the processing unit 32 identifies, as the second consumption characteristic, information indicating the amount of power consumption generated per unit traveling distance during turning for each radius of curvature.

[0050] In this way, the processing unit 32 determines the second consumption characteristic based on the target vehicle speed according to the driving environment (vehicle speed V0 calculated from the radius of curvature of the road) and the actual vehicle speed V1 of the vehicle 1.

[0051] Next, an example will be described in which third operation characteristic information is acquired in step S101 and third consumption characteristics are identified in step S102. The third operation characteristic information is operation characteristic information that indicates the tendency of the vehicle speed of the vehicle 1 during non-following driving, in which the driver of the vehicle 1 is not performing following driving.

[0052] For example, in step S101, the acquisition unit 31 acquires, as the third operation characteristic information, information indicating how high the vehicle speed of the vehicle 1 is traveling relative to an ideal vehicle speed V0 (target vehicle speed) that can keep running resistance to a certain level during non-following traveling. The ideal vehicle speed V0 may be set, for example, according to a legal speed limit. Specifically, the ideal vehicle speed V0 is set to the legal speed limit or a value obtained by subtracting a predetermined value from the legal speed limit. The acquisition unit 31 may acquire, for example, the legal speed limit set for the road on which the vehicle 1 is traveling, based on map data obtained from the navigation device 23 or information on a speed display board obtained by the surrounding environment sensor 12. The acquisition unit 31 acquires, for example, the amount of deviation between the vehicle speed V1 of the vehicle 1 during non-following traveling and the ideal vehicle speed V0 as the third operation characteristic information.

[0053] The acquisition unit 31 determines whether the vehicle 1 is not following a vehicle ahead based on, for example, the detection result of the surrounding environment sensor 12. For example, if the surrounding environment sensor 12 does not detect a leading vehicle, or if the inter-vehicle distance between the vehicle 1 and the leading vehicle detected by the surrounding environment sensor 12 is equal to or greater than a predetermined distance, the acquisition unit 31 can determine that the vehicle 1 is not following a vehicle ahead. The predetermined distance is, for example, 50 m.

[0054] If the third operation characteristic information is acquired in step S101, the processing unit 32 identifies a third consumption characteristic based on the third operation characteristic information in step S102. The third consumption characteristic is a power consumption characteristic during non-follow driving. The processing unit 32 stores the acquired third operation characteristic information in the memory 30b of the processing device 30, for example, and identifies the third consumption characteristic using the stored third operation characteristic information. This makes it possible to identify the third consumption characteristic using information stored in advance.

[0055] For example, the processing unit 32 identifies, as the third consumption characteristic, the characteristic of power consumption caused by the vehicle 1 traveling at a speed higher than the ideal vehicle speed V0 at which running resistance can be kept relatively low during non-following running. The higher the vehicle speed V1 of the vehicle 1 is relative to the ideal vehicle speed V0, the greater the running resistance acting on the vehicle 1. In this way, during non-following running, power consumption occurs due to the deviation between the vehicle speed V1 of the vehicle 1 and the ideal vehicle speed V0.

[0056] For example, the processing unit 32 estimates the amount of power consumption caused by the deviation between the vehicle speed V1 of the vehicle 1 during non-following traveling and the ideal vehicle speed V0 based on the amount of deviation between the vehicle speed V1 of the vehicle 1 during non-following traveling and the ideal vehicle speed V0. Specifically, the processing unit 32 estimates a larger value as the amount of power consumption caused by the deviation between the vehicle speed V1 of the vehicle 1 during non-following traveling and the ideal vehicle speed V0, the larger the vehicle speed V1 of the vehicle 1 during non-following traveling is and the larger the amount of deviation between the vehicle speed V1 and the vehicle speed V0. Then, based on the information obtained in this manner, the processing unit 32 identifies, for each speed, information indicating the amount of power consumption caused per unit traveling distance during non-following traveling.

[0057] In this way, the processing unit 32 identifies the third consumption characteristic based on the target vehicle speed according to the driving environment (vehicle speed V0 calculated from the legal speed limit on the road) and the actual vehicle speed V1 of the vehicle 1.

[0058] After step S102 , in step S103 , the processing unit 32 predicts the power consumption rate of the vehicle 1 .

[0059] In step S103, the processing unit 32 predicts the power consumption rate of the vehicle 1 based on the consumption characteristics identified in step S102. For example, if only some of the first, second, and third consumption characteristics are identified in step S102, the processing unit 32 predicts the power consumption rate of the vehicle 1 based on only some of the identified consumption characteristics in step S103. Alternatively, if all of the first, second, and third consumption characteristics are identified in step S102, the processing unit 32 predicts the power consumption rate of the vehicle 1 based on all of the first, second, and third consumption characteristics in step S103. Specifically, the processing unit 32 predicts the power consumption rate of the vehicle 1 by adjusting the power consumption rate of a standard vehicle 1 that does not take into account driver-specific driving operations by taking into account the above-mentioned consumption characteristics, which are characteristics of consumption of energy sources resulting from driver-specific driving operations.

[0060] For example, in predicting the power consumption rate taking the first consumption characteristic into consideration, the processing unit 32 first predicts the range of the driving route in which the vehicle 1 will perform follow-up driving. For example, the processing unit 32 predicts that the vehicle 1 will perform follow-up driving in a range of the driving route in which congestion occurs, using congestion information obtained from the navigation device 23. Then, the processing unit 32 predicts the power consumption rate of the vehicle 1, assuming that the vehicle 1 will perform follow-up driving in the range in which the vehicle 1 is predicted to perform follow-up driving, and that power will be consumed according to the first consumption characteristic.

[0061] Furthermore, for example, when predicting the power consumption rate taking the second consumption characteristic into account, the processing unit 32 first identifies the length of the turning section on the travel route. For example, the processing unit 32 identifies the length of the turning section for each curvature radius on the travel route using map data obtained from the navigation device 23. Then, the processing unit 32 predicts the power consumption rate of the vehicle 1, assuming that power will be consumed in accordance with the second consumption characteristic on the turning section.

[0062] Furthermore, for example, in predicting the power consumption rate taking the third consumption characteristic into consideration, the processing unit 32 first predicts a range of the driving route in which the vehicle 1 will not engage in follow-up driving. For example, the processing unit 32 predicts that the vehicle 1 will not engage in follow-up driving in a range of the driving route in which no congestion occurs, using traffic congestion information obtained from the navigation device 23. Then, the processing unit 32 predicts the power consumption rate of the vehicle 1, assuming that the vehicle 1 will not engage in follow-up driving in the range in which it is predicted that the vehicle 1 will not engage in follow-up driving and will consume power according to the third consumption characteristic.

[0063] After step S103, in step S104, the processing unit 32 predicts the cruising range of the vehicle 1 based on the power consumption rate of the vehicle 1, and the control flow shown in FIG. 3 ends.

[0064] In step S104, 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 power consumption rate of the vehicle 1. Specifically, the processing unit 32 can predict the cruising range as a value obtained by multiplying the remaining capacity of the battery 21 by the power consumption rate. Then, the processing unit 32 notifies the driver of the cruising range by, for example, causing the navigation device 23 to display the cruising range.

[0065] The processing unit 32 may store the consumption characteristic information identified in step S102 in the memory 30b of the processing device 30, and use the stored consumption characteristic information to predict the power consumption rate in step S103 and the cruising range in step S104. This allows the power consumption rate and the cruising range to be predicted appropriately based on, for example, multiple types of consumption characteristics. Furthermore, for example, when the system of the vehicle 1 is turned on, the power consumption rate and the cruising range can be predicted appropriately using information on consumption characteristics accumulated in the past.

[0066] As described above, in the first example, the processing device 30 identifies the consumption characteristics of the energy source of the vehicle 1 based on the target vehicle speed according to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1. Specifically, operation characteristic information, which is information indicating the driving operation characteristics unique to the driver of the vehicle 1, is acquired, and the consumption characteristics of the energy source of the vehicle 1 are identified based on the operation characteristic information. This makes it possible to grasp the consumption characteristics caused by the driving environment, and therefore to accurately predict the consumption characteristics of the energy source of the vehicle 1. In particular, in the first example, the processing device 30 predicts the cruising range of the vehicle 1 based on the consumption characteristics of the energy source. This makes it possible to accurately predict the cruising range of the vehicle 1.

[0067] Fig. 5 is a flowchart showing a second example of the flow of processing performed by the processing device 30. The control flow shown in Fig. 5 is executed repeatedly at preset time intervals, for example.

[0068] The control flow shown in FIG. 5 is a control flow in which steps S103 and S104 of the control flow shown in FIG. 3 are replaced with steps S201 and S202.

[0069] 5 , after step S102, in step S201, the processing unit 32 predicts the total amount of power consumption for each candidate travel route. Specifically, the processing unit 32 predicts the total amount of power consumption of the battery 21 when each candidate travel route is traveled. 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.

[0070] In step S201, the processing unit 32 predicts the total amount of power consumption for each candidate travel route based on the consumption characteristics identified in step S102. For example, the processing unit 32 predicts, as the total amount of power consumption for each candidate travel route, the total amount of power that would be consumed if the vehicle 1 traveled along each candidate travel route at a power consumption rate that is predicted by adjusting the power consumption rate of a standard vehicle 1 that does not take into account the driver's specific driving operation, by taking into account the above-mentioned consumption characteristics, which are the characteristics of consumption of energy sources resulting from the driver's specific driving operation. Note that the process for predicting the power consumption rate taking into account the consumption characteristics is the same as step S103 in FIG. 3 . However, in step S201, the processing unit 32 may predict the total amount of power consumption for each candidate travel route based on the consumption characteristics, without predicting the power consumption rate of the vehicle 1.

[0071] Following step S201, in step S202, the processing unit 32 determines a recommended driving route, and the control flow shown in FIG. 5 ends. As described above, the recommended driving route is a driving route that reduces power consumption. That is, in step S202, the processing unit 32 determines, as the recommended driving route, the driving route candidate that has the smallest predicted total power consumption. 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.

[0072] As described above, in the second example, similar to the first example described above, the consumption characteristics of the energy source of the vehicle 1 are identified based on the target vehicle speed according to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1, and therefore the consumption characteristics of the energy source of the vehicle 1 can be accurately predicted. In particular, in the second example, the processing device 30 determines a recommended driving route that suppresses consumption of the energy source based on the consumption characteristics of the energy source. This makes it possible to appropriately determine the recommended driving route.

[0073] The first and second examples have been described above as examples of processing performed by the processing device 30, with reference to the flowcharts of Fig. 3 and 5. However, the processing performed by the processing device 30 is not limited to the above examples.

[0074] For example, in the first and second examples, the acquisition unit 31 may not acquire any part of the first operation characteristic information, the second operation characteristic information, and the third operation characteristic information. In this case, in the first and second examples, the processing unit 32 does not identify consumption characteristics corresponding to the operation characteristic information that is not acquired among the first operation characteristic information, the second operation characteristic information, and the third operation characteristic information.

[0075] In addition, for example, in the first and second examples, the acquisition unit 31 may acquire operation characteristic information other than the first operation characteristic information, the second operation characteristic information, and the third operation characteristic information. Examples of the operation characteristic information include information indicating the characteristics of various driving operations under various circumstances, such as information indicating the tendency of the steering angle when the vehicle 1 is not changing lanes, or information indicating the tendency of acceleration / deceleration when the vehicle 1 is changing lanes.

[0076] Also, for example, the processing device 30 may predict the cruising range of the vehicle 1 based on the consumption characteristics of the energy source, as in the first example, and determine a recommended driving route based on the above consumption characteristics, as in the second example.

[0077] <Effects of the Processing Apparatus> Next, the effects of the processing apparatus 30 according to the embodiment of the present invention will be described.

[0078] The processor 30a of the processing device 30 according to this embodiment executes processing to identify the consumption characteristics of the energy source of the vehicle 1 based on a target vehicle speed according to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1. This makes it possible to grasp the consumption characteristics caused by the driving environment, and therefore to accurately predict the consumption characteristics of the energy source of the vehicle 1.

[0079] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment executes processing that includes predicting, based on the consumption characteristics, the cruising range of the vehicle 1. This allows the cruising range of the vehicle 1 to be predicted using the consumption characteristics of the energy source according to the driving environment of the vehicle 1, thereby enabling the cruising range of the vehicle 1 to be predicted with greater accuracy.

[0080] Furthermore, the processor 30a of the processing device 30 according to this embodiment preferably executes processing that includes determining a recommended driving route that reduces consumption of energy sources based on the consumption characteristics. This allows the recommended driving route to be determined using the consumption characteristics of the energy sources according to the driving environment of the vehicle 1, thereby making it possible to appropriately determine the recommended driving route.

[0081] Furthermore, when the vehicle 1 is following a preceding vehicle, the processor 30a of the processing device 30 according to this embodiment preferably sets a target vehicle speed corresponding to the vehicle speed of the preceding vehicle and executes a process of identifying consumption characteristics based on the deviation between the target vehicle speed and the actual vehicle speed. This makes it possible to grasp the consumption characteristics resulting from following the vehicle, thereby more accurately predicting the consumption characteristics of the energy source of the vehicle 1.

[0082] In particular, in an example in which the processor 30a identifies consumption characteristics during follow-up driving, the processor 30a preferably determines that the vehicle 1 is performing follow-up driving when the surrounding environment sensor 12 detects a preceding vehicle and the inter-vehicle distance between the vehicle 1 and the preceding vehicle is less than a predetermined distance. This makes it possible to appropriately determine whether the vehicle 1 is performing follow-up driving by utilizing the surrounding environment information detected by the surrounding environment sensor 12.

[0083] Furthermore, when the vehicle 1 is turning, the processor 30a of the processing device 30 according to this embodiment preferably sets a target vehicle speed according to the radius of curvature of the road on which the vehicle 1 is traveling, and executes a process of identifying consumption characteristics based on the deviation between the target vehicle speed and the actual vehicle speed. This makes it possible to grasp the consumption characteristics resulting from turning, and therefore to more accurately predict the consumption characteristics of the energy source of the vehicle 1.

[0084] In particular, in an example in which the processor 30a identifies the consumption characteristics during cornering, it is preferable to set the target vehicle speed based on the upper limit vehicle speed range set according to the curvature radius of the roadway, within which tire slip does not occur on the vehicle 1. This makes it possible to more accurately predict the consumption characteristics of the energy source of the vehicle 1 after properly understanding the consumption characteristics resulting from cornering.

[0085] Furthermore, the processor 30a of the processing device 30 according to this embodiment preferably sets a target vehicle speed corresponding to the legal speed limit of the road on which the vehicle 1 is traveling during non-following driving, in which the vehicle 1 is not following a preceding vehicle, and executes processing to identify consumption characteristics based on the deviation between the target vehicle speed and the actual vehicle speed. This makes it possible to grasp consumption characteristics resulting from non-following driving, thereby more accurately predicting the consumption characteristics of the energy source of the vehicle 1.

[0086] In particular, in an example in which the processor 30a identifies consumption characteristics during non-following driving, the processor 30a preferably determines that the vehicle 1 is not performing follow-up driving when the surrounding environment sensor 12 does not detect a preceding vehicle, or when the surrounding environment sensor 12 detects a preceding vehicle and the inter-vehicle distance between the vehicle 1 and the preceding vehicle is equal to or greater than a predetermined distance. This makes it possible to appropriately determine whether the vehicle 1 is performing follow-up driving by utilizing the surrounding environment information detected by the surrounding environment sensor 12.

[0087] 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.

[0088] 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.

[0089] REFERENCE SIGNS LIST 1 vehicle 11 vehicle speed sensor 12 ambient environment sensor 13 yaw rate sensor 21 battery 22 traction 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; and The processor executes a process including, during follow-up driving in which a driver of a vehicle is following a preceding vehicle, setting a target vehicle speed according to a driving environment of the vehicle, the target vehicle speed being in accordance with the vehicle speed of the preceding vehicle, and specifying a consumption characteristic of an energy source of the vehicle based on a deviation amount between the target vehicle speed and an actual vehicle speed of the vehicle. Processing equipment.

2. the vehicle includes an ambient environment sensor that detects ambient environment information related to an ambient environment of the vehicle; the processor determines that the vehicle is in the following driving mode when the surrounding environment sensor detects the preceding vehicle and the inter-vehicle distance between the vehicle and the preceding vehicle is less than a predetermined distance; The processing device of claim 1 .

3. One or more processors; one or more memories coupled to the processor; and the processor executes a process including, when the vehicle is turning, setting a target vehicle speed according to a curvature radius of a road on which the vehicle is traveling as a target vehicle speed according to a traveling environment of the vehicle, and specifying a consumption characteristic of an energy source of the vehicle based on a deviation amount between the target vehicle speed and an actual vehicle speed of the vehicle. Processing equipment.

4. the processor sets the target vehicle speed based on an upper limit vehicle speed range in which tire slip does not occur, the upper limit vehicle speed being set in accordance with a curvature radius of the road; The processing device according to claim 3 .

5. One or more processors; one or more memories coupled to the processor; and the processor executes a process including, during non-following driving in which the driver of the vehicle is not following a preceding vehicle, setting a target vehicle speed according to the driving environment of the vehicle, the target vehicle speed according to the legal speed limit of the road on which the vehicle is traveling, and specifying a consumption characteristic of an energy source of the vehicle based on a deviation amount between the target vehicle speed and the actual vehicle speed of the vehicle; the vehicle includes an ambient environment sensor that detects ambient environment information related to an ambient environment of the vehicle; The processor determines that the vehicle is not performing the following driving when the surrounding environment sensor has not detected the preceding vehicle, or when the surrounding environment sensor has detected the preceding vehicle and the inter-vehicle distance between the vehicle and the preceding vehicle is equal to or greater than a predetermined distance. Processing equipment.