Processing unit

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

Technical Field

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

[0002] Vehicles travel by consuming an energy source mounted on the vehicle. For example, in an electric vehicle, electric power stored in a battery is used as the energy source. In order to support the driving of a 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 Japanese Unexamined Patent Application Publication No. 2012-220415 Summary of Invention Problems to be Solved by the Invention

[0004] In supporting the driving of a vehicle driver, it is desirable to predict the consumption characteristics of the energy source of the vehicle with higher accuracy.

[0005] Accordingly, an object of the present invention is to provide a processing device capable of predicting the consumption characteristics of the energy source of a vehicle with higher accuracy. Means for Solving the Problems

[0006] To solve the above problem, a processing device according to an embodiment of the present invention comprises: one or more processors, and one or more memories connected to the processors, , the processors During follow-me driving, in which the vehicle driver is following a preceding vehicle, the system performs a process that includes setting a target vehicle speed corresponding to the speed of the preceding vehicle as the target vehicle speed according to the driving environment of the vehicle, and identifying the energy source consumption characteristics of the vehicle based on the difference between the target vehicle speed and the actual vehicle speed of the vehicle. . To solve the above problems, the processing apparatus according to one embodiment of the present invention is: One or more processors, One or more memory connected to the processor, It has, The processor, when the vehicle is turning, sets a target vehicle speed corresponding to the radius of curvature of the road on which the vehicle is traveling, and performs processing that includes identifying the energy consumption characteristics of the vehicle's energy source based on the difference between the target vehicle speed and the actual vehicle speed. To solve the above problems, the processing apparatus according to one embodiment of the present invention is: One or more processors, One or more memory connected to the processor, It has, The processor, in non-follow driving mode when the vehicle driver is not following a preceding vehicle, sets a target vehicle speed corresponding to the legal speed limit of the road on which the vehicle is traveling as a target vehicle speed according to the driving environment of the vehicle, and performs processing that includes identifying the energy source consumption characteristics of the vehicle based on the difference between the target vehicle speed and the actual vehicle speed of the vehicle. The vehicle is equipped with an ambient environment sensor that detects ambient environment information relating to the environment surrounding the vehicle. The processor determines that the vehicle is not performing follow-up driving when the preceding vehicle is not detected by the surrounding environment sensor, or when the preceding vehicle is detected by the surrounding environment sensor and the distance between the vehicle and the preceding vehicle is greater than or equal to a predetermined distance. [Effects of the Invention]

[0007] According to the present invention, it becomes possible to predict the energy consumption characteristics of a vehicle's energy source with greater accuracy. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a schematic diagram showing the general configuration of a vehicle according to an embodiment of the present invention. [Figure 2] Figure 2 is a block diagram showing an example of the functional configuration of a processing apparatus according to an embodiment of the present invention. [Figure 3] Figure 3 is a flowchart showing a first example of the processing flow performed by the processing apparatus according to an embodiment of the present invention. [Figure 4] Figure 4 is a diagram illustrating the process of acquiring first operational characteristic information and the process of identifying first consumption characteristics performed by the processing apparatus according to an embodiment of the present invention. [Figure 5] Figure 5 is a flowchart showing a second example of the processing flow performed by the processing apparatus according to an embodiment of the present invention. [Modes for carrying out the invention]

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

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

[0011] Figure 1 is a schematic diagram showing a general configuration of the vehicle 1. In the following, an example is described in which the vehicle 1 is an electric vehicle equipped with a battery 21, and travels using electric 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 electric 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 travel using fuel supplied to the engine as the energy source. In that case, the travel distance per unit capacity of the energy source is the fuel consumption rate.

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

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

[0015] The ambient environment sensor 12 detects ambient environment information related to the environment surrounding the vehicle 1. Examples of ambient environment sensors 12 include cameras, radar, or ultrasonic sensors. Specifically, the ambient environment sensor 12 is installed at the front of the vehicle 1 and detects ambient environment information in front of the vehicle 1. For example, the ambient environment sensor 12 detects ambient environment information related to the behavior of a preceding vehicle traveling in front of the vehicle 1. Behavior information includes, for example, speed and acceleration. Also, for example, the ambient environment sensor 12 detects ambient environment information related to the positional relationship between the road on which the vehicle 1 is traveling and the vehicle 1. Positional relationship information includes, for example, 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. Examples of batteries used for the battery 21 include lithium-ion batteries, lithium-ion polymer batteries, nickel-metal hydride batteries, nickel-cadmium batteries, and lead-acid batteries, but other types of batteries may also be used. The battery 21 stores the power supplied to the traction motor 22.

[0018] The traction motor 22 outputs power transmitted to the wheels of the vehicle 1 and is composed of, for example, a three-phase AC motor. The traction motor 22 is driven using the power of the battery 21 to output power. In addition, the traction motor 22 can be regenerated during the deceleration of the vehicle 1 and generate electricity using the kinetic energy of the wheels. In this case, the electricity generated by the traction motor 22 is supplied to the battery 21. As a result, the battery 21 is charged by the electricity generated by the traction motor 22.

[0019] The navigation device 23 is a device that guides the user on a driving route from the current location of vehicle 1 to a destination desired by the user, in response to input operations by the driver of vehicle 1. The navigation device 23 determines a recommended driving route from among several driving route candidates and proposes the determined recommended driving route to the user. In this specification, the recommended driving route is a driving route that suppresses the consumption of energy sources. The driver selects a driving route to be guided by the navigation device 23 from among several driving route candidates, referring to the recommended driving route.

[0020] Specifically, the navigation device 23 has a function to visually display information and displays various information related to route guidance. Examples of information displayed by the navigation device 23 include the current location of vehicle 1, the driving route to be guided, the location of the destination, the distance on the driving route from the current location of vehicle 1 to the destination, and the estimated time to reach the destination. The navigation device 23 can acquire information indicating the current location of vehicle 1 based on signals transmitted from GPS (Global Positioning System) satellites.

[0021] The processing unit 30 includes one or more processors 30a and one or more memories 30b connected to the processors 30a. The processors 30a include, for example, a CPU (Central Processing Unit). The memories 30b include, for example, ROM (Read Only Memory) and RAM (Random Access Memory). ROM is a memory element that stores programs and arithmetic parameters used by the CPU. RAM is a memory element that temporarily stores data such as variables and parameters used in processing executed by the CPU.

[0022] The processing unit 30 communicates with various devices within 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. Communication between the processing unit 30 and each device is achieved, for example, using CAN (Controller Area Network) communication.

[0023] Figure 2 is a block diagram showing an example of the functional configuration of the processing unit 30. For example, as shown in Figure 2, the processing unit 30 has an acquisition unit 31 and a processing unit 32. Various processes, including those described below, performed by the acquisition unit 31 or the processing unit 32, can be executed by the processor 30a. In detail, various processes are executed by the processor 30a executing a program stored in memory 30b.

[0024] The acquisition unit 31 acquires various types of information and outputs them to the processing unit 32. For example, the acquisition unit 31 acquires information from the vehicle speed sensor 11, the ambient environment sensor 12, the yaw rate sensor 13, and the navigation device 23. In this specification, information acquisition may include information extraction or generation. Information generation is, 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 to control the operation of the navigation device 23.

[0026] The functions of the processing unit 30 according to this embodiment may be divided among multiple devices, or multiple functions may be implemented by a single device. If the functions of the processing unit 30 are divided among multiple devices, these multiple devices may be connected to each other via a communication bus such as CAN.

[0027] <Operation of the processing unit> Next, the operation of the processing apparatus 30 according to an embodiment of the present invention will be described with reference to Figures 3 to 5.

[0028] In this embodiment, the processing unit 30 identifies the energy source consumption characteristics of vehicle 1 based on the target vehicle speed corresponding to the driving environment of vehicle 1 and the actual vehicle speed of vehicle 1. Specifically, it acquires operation characteristic information, which is information indicating the unique driving operation characteristics of the driver of vehicle 1, based on the target vehicle speed corresponding to the driving environment of vehicle 1 and the actual vehicle speed of vehicle 1, and identifies the energy source consumption characteristics of vehicle 1 based on the operation characteristic information. As a result, as will be described later, the energy source consumption characteristics of vehicle 1 can be predicted with greater accuracy. In the following example, the energy source is electricity.

[0029] Driving operation characteristics refer to the characteristics of the driver's driving operations when operating vehicle 1, such as the driver's specific driving habits. Specifically, the driver's driving operations are reflected in the behavior of vehicle 1, such as its speed. Therefore, the processing unit 30 collects information on the behavior of vehicle 1 while it is driving and acquires operation characteristic information based on information on the behavior of vehicle 1 collected in the past. The energy source consumption characteristics of vehicle 1 refer to the characteristics of energy source consumption caused by the driver's specific driving operations.

[0030] The following describes the first and second examples of processing performed by the processing unit 30. In the following examples, the first, second, and third operational characteristic information are acquired as operational characteristic information, and the first, second, and third consumption characteristics are identified as consumption characteristics. However, as will be discussed later, the acquired operational characteristic information and identified consumption characteristics are not limited to the following examples.

[0031] Figure 3 is a flowchart showing a first example of the processing flow performed by the processing unit 30. The control flow shown in Figure 3 is executed repeatedly, for example, at a predetermined time interval.

[0032] When the control flow shown in Figure 3 is initiated, first, in step S101, the acquisition unit 31 acquires operation characteristic information. Following step S101, in step S102, the processing unit 32 identifies the consumption characteristics based on the operation characteristic information.

[0033] First, we will explain an example in which the first operational characteristic information is acquired in step S101 and the first consumption characteristic is identified in step S102. The first operational characteristic information is operational characteristic information that shows the acceleration and deceleration tendency of vehicle 1 when vehicle 1 is following another vehicle. Following another vehicle means driving in which the driver of vehicle 1 follows a preceding vehicle.

[0034] For example, in step S101, the acquisition unit 31 acquires information as first operation characteristic information indicating how much the vehicle 1 is accelerating or decelerating relative to an ideal vehicle speed (target vehicle speed) in which excessive acceleration or deceleration does not occur during follow-me driving of the vehicle 1.

[0035] The acquisition unit 31 determines, for example, whether vehicle 1 is following another vehicle based on the detection results of the ambient environment sensor 12. For example, if the ambient environment sensor 12 detects a preceding vehicle, and the distance between vehicle 1 and the preceding vehicle detected by the ambient environment sensor 12 is less than a predetermined distance, the acquisition unit 31 can determine that vehicle 1 is following another vehicle. The predetermined distance is, for example, 50m.

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

[0037] Figure 4 is a diagram illustrating the acquisition process of first operation characteristic information performed by the processing unit 30, and the identification process of the first consumption characteristic, which will be described later. In Figure 4, the horizontal axis T represents time, and the vertical axis V represents vehicle speed. For example, the acquisition unit 31 first estimates the transition of an ideal vehicle speed V0 without 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 subject to excessive acceleration or deceleration due to the driving operations of the driver of the preceding vehicle. Therefore, the acquisition unit 31 can estimate the transition of an ideal vehicle speed V0 without excessive acceleration or deceleration by, for example, applying a filter such as a low-pass filter to the vehicle speed of the preceding vehicle.

[0038] The acquisition unit 31 then acquires the result of comparing the actual vehicle speed V1 of vehicle 1, detected by the vehicle speed sensor 11 during follow-up driving, with the ideal vehicle speed V0 during follow-up driving as first operation characteristic information. For example, in region R1 in Figure 4, the vehicle speed V1 of vehicle 1 is higher than the ideal vehicle speed V0, indicating that vehicle 1 is accelerating excessively. On the other hand, in region R2 in Figure 4, the vehicle speed V1 of vehicle 1 is lower than the ideal vehicle speed V0, indicating that vehicle 1 is decelerating excessively. The acquisition unit 31 acquires information such as the frequency of occurrence of region R1, the amount of deviation between vehicle speed V1 and vehicle speed V0 in region R1, the frequency of occurrence of region R2, and the amount of deviation between vehicle speed V1 and vehicle speed V0 in region R2 as first operation characteristic information.

[0039] When the first operation characteristic information is acquired in step S101, in step S102, the processing unit 32 identifies the first consumption characteristic based on the first operation characteristic information. The first consumption characteristic is the power consumption characteristic during follow-up driving. The processing unit 32, for example, stores the acquired first operation characteristic information in the memory 30b of the processing unit 30, and uses the stored first operation characteristic information to identify the first consumption characteristic. This allows the first consumption characteristic to be identified using previously accumulated information.

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

[0041] For example, the processing unit 32 estimates the amount of power consumed due to excessive acceleration based on the difference between vehicle speed V1 and vehicle speed V0 in each region R1. In estimating the above amount of power consumption, the processing unit 32 may also take into account the weight of vehicle 1. Then, the processing unit 32 estimates the amount of power regenerated due to excessive deceleration based on the difference between vehicle speed V1 and vehicle speed V0 in each region R2. Then, the processing unit 32 estimates the amount of power consumed due to excessive acceleration and deceleration during follow-up driving by subtracting the amount of power regenerated due to excessive deceleration from the amount of power consumed due to excessive acceleration. For example, based on the information obtained in this way, the processing unit 32 identifies information indicating the amount of power consumed per unit distance during follow-up driving as a first consumption characteristic.

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

[0043] Next, we will explain an example 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 shows the acceleration and deceleration tendency of vehicle 1 when vehicle 1 is turning.

[0044] For example, in step S101, the acquisition unit 31 acquires information as second operation characteristic information indicating how much 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, for example, whether or not the vehicle 1 is turning, based on the detection result of the yaw rate sensor 13. For example, if the yaw rate detected by the yaw rate sensor 13 is large enough, 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 and the vehicle speed of the vehicle 1 detected by the vehicle speed sensor 11 when the vehicle 1 is turning. 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 ideal vehicle speed V0 could be, for example, a vehicle speed obtained by multiplying the upper limit vehicle speed within the range where tire slip does not occur during turning by a predetermined safety factor. Here, the upper limit vehicle speed is a value predetermined for each radius of curvature of the road, and the predetermined safety factor is, for example, 80%. In identifying the upper limit vehicle speed, the acquisition unit 31 may also take into account the weight of the vehicle 1 or the friction characteristics of the tires. The acquisition unit 31 then acquires the result of comparing the vehicle speed V1 of vehicle 1 detected by the vehicle speed sensor 11 during turning with the ideal vehicle speed V0 during turning as second operational characteristic information. For example, the acquisition unit 31 acquires the amount of deviation between the vehicle speed V1 and the vehicle speed V0 during turning as second operational characteristic information for each of multiple turning maneuvers.

[0047] If the second operation characteristic information is acquired in step S101, in step S102, the processing unit 32 identifies the second consumption characteristic based on the second operation characteristic information. The second consumption characteristic is the power consumption characteristic during turning. The processing unit 32, for example, stores the acquired second operation characteristic information in the memory 30b of the processing unit 30, and uses the stored second operation characteristic information to identify the second consumption characteristic. This allows the second consumption characteristic to be identified using previously accumulated information.

[0048] For example, the processing unit 32 identifies the power consumption characteristics resulting from excessive acceleration and deceleration by the vehicle 1 during cornering as a second power consumption characteristic. When the vehicle 1 decelerates unnecessarily relative to the ideal vehicle speed corresponding to the radius of curvature of the road during cornering, it becomes necessary to accelerate unnecessarily. Thus, power consumption occurs when the vehicle 1 performs excessive acceleration and deceleration during cornering.

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

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

[0051] Next, we will explain an example in which third operational characteristic information is acquired in step S101 and third consumption characteristics are identified in step S102. The third operational characteristic information is operational characteristic information that shows the trend of the vehicle speed of vehicle 1 when the driver of vehicle 1 is not performing follow-up driving.

[0052] For example, in step S101, the acquisition unit 31 acquires information as third operational characteristic information indicating how high the speed of vehicle 1 is relative to an ideal vehicle speed V0 (target vehicle speed) that can keep driving resistance relatively low when vehicle 1 is not following another vehicle. The above-mentioned ideal vehicle speed V0 can be set, for example, according to the legal speed limit. Specifically, the above-mentioned 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 can acquire the legal speed limit set for the road on which vehicle 1 is traveling, for example, based on map data obtained from the navigation device 23, or information from 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 vehicle 1 and the ideal vehicle speed V0 during non-following driving as third operational characteristic information.

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

[0054] If third operation characteristic information is acquired in step S101, in step S102, the processing unit 32 identifies the third consumption characteristic based on the third operation characteristic information. The third consumption characteristic is the power consumption characteristic during non-following driving. The processing unit 32, for example, stores the acquired third operation characteristic information in the memory 30b of the processing unit 30, and uses the stored third operation characteristic information to identify the third consumption characteristic. This allows the third consumption characteristic to be identified using previously accumulated information.

[0055] For example, the processing unit 32 identifies a third power consumption characteristic as the power consumption characteristic resulting from the vehicle 1 traveling at a speed higher than the ideal vehicle speed V0, which allows for a certain degree of reduced driving resistance during non-following driving. The higher the vehicle speed V1 of vehicle 1 is compared to the ideal vehicle speed V0, the greater the driving resistance acting on vehicle 1. Thus, during non-following driving, power consumption occurs due to the discrepancy between the vehicle speed V1 of vehicle 1 and the ideal vehicle speed V0.

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

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

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

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

[0060] For example, in predicting the power consumption rate that takes into account the first consumption characteristics, the processing unit 32 first predicts the range in the driving route where vehicle 1 will perform follow-up driving. For example, the processing unit 32 uses traffic congestion information obtained from the navigation device 23 to predict that vehicle 1 will perform follow-up driving in the range of the driving route where congestion occurs. Then, the processing unit 32 assumes that vehicle 1 will perform follow-up driving in the range in which it is predicted that vehicle 1 will perform follow-up driving, and that power consumption according to the first consumption characteristics will occur, and predicts the power consumption rate of vehicle 1.

[0061] Furthermore, in predicting the power consumption rate that takes into account the second consumption characteristic, for example, the processing unit 32 first identifies the distance of the turns in the driving route. For example, the processing unit 32 uses map data obtained from the navigation device 23 to identify the distance of the turns for each radius of curvature in the driving route. Then, the processing unit 32 assumes that power consumption occurs in the turns according to the second consumption characteristic and predicts the power consumption rate of the vehicle 1.

[0062] Furthermore, for example, in predicting the power consumption rate that takes into account the third consumption characteristic, the processing unit 32 first predicts the range in the driving route in which vehicle 1 will not perform follow-up driving. For example, the processing unit 32 uses traffic congestion information obtained from the navigation device 23 to predict that vehicle 1 will not perform follow-up driving in the range of the driving route in which no traffic congestion occurs. Then, the processing unit 32 assumes that vehicle 1 will not perform follow-up driving in the range in which it is predicted that vehicle 1 will not perform follow-up driving, and that power consumption due to the third consumption characteristic will occur, and predicts the power consumption rate of vehicle 1.

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

[0064] In step S104, for example, the processing unit 32 predicts the remaining 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 remaining range as a value obtained by multiplying the remaining capacity of the battery 21 by the power consumption rate. The processing unit 32 then informs the driver of the remaining range, for example, by displaying the remaining range on the navigation device 23.

[0065] Furthermore, the processing unit 32 can, for example, store the consumption characteristic information identified in step S102 in the memory 30b of the processing unit 30, and use the stored consumption characteristic information to predict the power consumption rate in step S103 and the remaining driving range in step S104. This allows for the appropriate prediction of the power consumption rate and the remaining driving range based on multiple types of consumption characteristics. Additionally, for example, when the system of vehicle 1 is turned on, it is possible to appropriately predict the power consumption rate and the remaining driving range using previously accumulated consumption characteristic information.

[0066] As explained above, in the first example, the processing unit 30 identifies the energy source consumption characteristics of vehicle 1 based on the target vehicle speed corresponding to the driving environment of vehicle 1 and the actual vehicle speed of vehicle 1. Specifically, it acquires operation characteristic information, which is information indicating the unique driving operation characteristics of the driver of vehicle 1, and identifies the energy source consumption characteristics of vehicle 1 based on the operation characteristic information. As a result, it is possible to grasp the consumption characteristics caused by the driving environment and predict the energy source consumption characteristics of vehicle 1 with high accuracy. In particular, in the first example, the processing unit 30 predicts the remaining driving range of vehicle 1 based on the energy source consumption characteristics. As a result, it is possible to predict the remaining driving range of vehicle 1 with high accuracy.

[0067] Figure 5 is a flowchart showing a second example of the processing flow performed by the processing unit 30. The control flow shown in Figure 5 is executed repeatedly, for example, at a predetermined time interval.

[0068] The control flow shown in Figure 5 is the same as the control flow shown in Figure 3, but with steps S103 and S104 replaced by steps S201 and S202.

[0069] In the control flow shown in Figure 5, following step S102, in step S201, the processing unit 32 predicts the total power consumption of each candidate driving route. Specifically, the processing unit 32 predicts the total power consumption of the battery 21 when each candidate driving route is traveled. The candidate driving routes are extracted by the navigation device 23, for example, based on the current location of the vehicle 1 and the destination desired by the user.

[0070] In step S201, the processing unit 32 predicts the total power consumption of each candidate route based on the consumption characteristics identified in step S102. For example, the processing unit 32 predicts the total power consumption of each candidate route if vehicle 1 were to travel along the route at a predicted power consumption rate that takes into account the above-mentioned consumption characteristics, which are the characteristics of energy source consumption caused by driver-specific driving operations, compared to the standard power consumption rate of vehicle 1 which does not take into account driver-specific driving operations. The processing for predicting the power consumption rate that takes into account each consumption characteristic is the same as in step S103 in Figure 3. However, in step S201, the processing unit 32 may predict the total power consumption of each candidate route based on each consumption characteristic without predicting the power consumption rate of vehicle 1.

[0071] Following step S201, in step S202, the processing unit 32 determines the recommended driving route, and the control flow shown in Figure 5 ends. As described above, the recommended driving route is a driving route that suppresses power consumption. In other words, in step S202, the processing unit 32 determines the candidate driving route with the smallest predicted total power consumption as the recommended driving route. The processing unit 32 then informs the driver of the recommended driving route, for example, by displaying the recommended driving route on the navigation device 23.

[0072] As explained above, in the second example, similar to the first example described above, the energy consumption characteristics of the vehicle 1 are identified based on the target vehicle speed corresponding to the driving environment of the vehicle 1 and the actual vehicle speed of the vehicle 1, thereby enabling accurate prediction of the energy consumption characteristics of the vehicle 1. In particular, in the second example, the processing unit 30 determines a recommended driving route that suppresses energy consumption based on the energy consumption characteristics of the energy source. This allows for the appropriate determination of the recommended driving route.

[0073] In the above, the first and second examples of processing performed by the processing unit 30 were described with reference to the flowcharts in Figure 3 and Figure 5. However, the processing performed by the processing unit 30 is not limited to the above examples.

[0074] For example, in the first and second examples, the acquisition unit 31 does not need to acquire any part of the first operation characteristic information, the second operation characteristic information, and the third operation characteristic information. In that case, in the first and second examples, the processing unit 32 does not identify the consumption characteristics that correspond 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] Furthermore, in the first and second examples, for example, 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 operation characteristic information include information that shows the characteristics of various driving operations under various conditions, and may include, for example, information that shows the tendency of the steering angle when the vehicle 1 is not changing lanes, or information that shows the tendency of acceleration and deceleration when the vehicle 1 changes lanes.

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

[0077] <Effects of the treatment device> 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 unit 30 according to this embodiment performs a process to identify the energy consumption characteristics of the vehicle 1's energy source based on the target vehicle speed corresponding to the vehicle 1's driving environment and the actual vehicle speed of the vehicle 1. As a result, the energy consumption characteristics caused by the driving environment can be grasped, and the energy consumption characteristics of the vehicle 1 can be predicted with high accuracy.

[0079] Furthermore, it is preferable that the processor 30a of the processing unit 30 according to this embodiment performs processing that includes predicting the cruising range of the vehicle 1 based on its consumption characteristics. This allows for a more accurate prediction of the cruising range of the vehicle 1, as it is possible to predict the cruising range of the vehicle 1 using the energy source consumption characteristics according to the driving environment of the vehicle 1.

[0080] Furthermore, it is preferable that the processor 30a of the processing apparatus 30 according to this embodiment performs processing that includes determining a recommended driving route that suppresses the consumption of energy sources based on consumption characteristics. This allows the recommended driving route to be determined appropriately by using the energy source consumption characteristics according to the driving environment of the vehicle 1.

[0081] Furthermore, in the processing device 30 according to this embodiment, it is preferable that the processor 30a sets a target vehicle speed corresponding to the vehicle speed of the preceding vehicle when the vehicle 1 is performing follow-up driving in which it follows a preceding vehicle, and performs processing to identify consumption characteristics based on the difference between the target vehicle speed and the actual vehicle speed. This makes it possible to understand the consumption characteristics caused by follow-up driving, and thus predict the energy source consumption characteristics of the vehicle 1 with greater accuracy.

[0082] In particular, in an example where the processor 30a identifies the power consumption characteristics during follow-up driving, it is preferable that the processor 30a determines that vehicle 1 is performing follow-up driving when the surrounding environment sensor 12 has detected a preceding vehicle and the distance between vehicle 1 and the preceding vehicle is less than a predetermined distance. This allows for an appropriate determination of whether or not vehicle 1 is performing follow-up driving by utilizing the surrounding environment information detected by the surrounding environment sensor 12.

[0083] Furthermore, it is preferable that the processor 30a of the processing device 30 according to this embodiment sets a target vehicle speed corresponding to the radius of curvature of the road on which the vehicle 1 is traveling as the target vehicle speed when the vehicle 1 is turning, and performs a process to identify the consumption characteristics based on the difference between the target vehicle speed and the actual vehicle speed. This makes it possible to understand the consumption characteristics caused by turning, and thus predict the consumption characteristics of the energy source of the vehicle 1 with greater accuracy.

[0084] In particular, in the example where the processor 30a identifies the consumption characteristics during cornering, it is preferable to set the target vehicle speed based on the upper limit of vehicle speed within the range in which no slippage occurs in the tires of vehicle 1, which is set according to the radius of curvature of the road. This makes it possible to accurately predict the consumption characteristics of the energy source of vehicle 1 after appropriately understanding the consumption characteristics caused by cornering.

[0085] Furthermore, in the processing device 30 according to this embodiment, it is preferable that the processor 30a sets a target vehicle speed corresponding to the legal speed limit of the road on which the vehicle 1 is traveling as the target vehicle speed when the vehicle 1 is not following a preceding vehicle, and performs a process to identify consumption characteristics based on the difference between the target vehicle speed and the actual vehicle speed. This makes it possible to understand the consumption characteristics caused by non-following driving, and thus predict the energy source consumption characteristics of the vehicle 1 with greater accuracy.

[0086] In particular, in an example where the processor 30a identifies the power consumption characteristics during non-following driving, it is preferable that the processor 30a determines that vehicle 1 is not following a vehicle if the surrounding environment sensor 12 does not detect a preceding vehicle, or if the surrounding environment sensor 12 detects a preceding vehicle and the distance between vehicle 1 and the preceding vehicle is greater than or equal to a predetermined distance. This allows for an appropriate determination of whether or not vehicle 1 is following a vehicle by utilizing the surrounding environment information detected by the surrounding environment sensor 12.

[0087] Preferred embodiments of the present invention have been described above with reference to the attached drawings. However, it goes without saying that the present invention is not limited to the embodiments described above, and that various modifications or alterations within the scope of the claims also fall within the technical scope of the present invention.

[0088] For example, the processes described using flowcharts in this specification do not necessarily have to be performed in the order shown in the flowcharts. Additional processing steps may be adopted, and some processing steps may be omitted. [Explanation of Symbols]

[0089] 1 vehicle 11. Vehicle speed sensor 12. Ambient environment sensors 13 Yaw rate sensor 21 batteries 22. Motor for driving 23 Navigation System 30 Processing Unit 30a Processor 30b memory 31 Acquisition Department 32 Processing Units

Claims

1. One or more processors, One or more memories connected to the processor, It has, The processor, during follow-drive driving in which the vehicle driver follows a preceding vehicle, performs a process that includes setting a target vehicle speed corresponding to the speed of the preceding vehicle as a target vehicle speed according to the driving environment of the vehicle, and identifying the energy source consumption characteristics of the vehicle based on the difference between the target vehicle speed and the actual vehicle speed of the vehicle. Processing device.

2. The vehicle is equipped with an ambient environment sensor that detects ambient environment information relating to the environment surrounding the vehicle. The processor determines that the vehicle is performing follow-up driving when the preceding vehicle is detected by the surrounding environment sensor and the distance between the vehicle and the preceding vehicle is less than a predetermined distance. The apparatus according to claim 1.

3. One or more processors, One or more memories connected to the processor, It has, The processor, when the vehicle is turning, sets a target vehicle speed corresponding to the radius of curvature of the road on which the vehicle is traveling, and performs processing that includes identifying the energy consumption characteristics of the vehicle's energy source based on the difference between the target vehicle speed and the actual vehicle speed. Processing device.

4. The processor sets the target vehicle speed based on an upper limit vehicle speed within a range in which the vehicle's tires do not slip, which is set according to the radius of curvature of the road. The apparatus according to claim 3.

5. One or more processors, One or more memories connected to the processor, It has, The processor, in non-follow driving mode when the vehicle driver is not following a preceding vehicle, sets a target vehicle speed corresponding to the legal speed limit of the road on which the vehicle is traveling as a target vehicle speed according to the driving environment of the vehicle, and performs processing that includes identifying the energy source consumption characteristics of the vehicle based on the difference between the target vehicle speed and the actual vehicle speed of the vehicle. The vehicle is equipped with an ambient environment sensor that detects ambient environment information relating to the environment surrounding the vehicle. The processor determines that the vehicle is not performing follow-up driving when the preceding vehicle is not detected by the surrounding environment sensor, or when the preceding vehicle is detected by the surrounding environment sensor and the distance between the vehicle and the preceding vehicle is greater than or equal to a predetermined distance. Processing device.

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