Vehicle energy consumption prediction system, vehicle energy consumption prediction program, and prediction method

Abstract

To provide a vehicle energy consumption prediction system capable of predicting the energy consumption of a vehicle for each driving route, a vehicle energy consumption prediction program, and a prediction method.SOLUTION: The vehicle energy consumption prediction system includes: a driving conditions acquisition unit 102 that acquires driving conditions including information about the number of travel times the vehicle V on the predetermined driving route R and the wear condition of the tires mounted on a vehicle; a road severity acquisition unit 103 that acquires road severity RS1 corresponding to the energy consumption of the vehicle per distance traveled for each driving route; and an energy consumption prediction unit 105 that predicts the energy consumption of the vehicle based on the driving conditions and the road severity.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an energy consumption prediction system for predicting the energy consumption of a bus, a freight vehicle, or the like, a vehicle energy consumption prediction program, and a vehicle energy consumption prediction method. [Background technology]

[0002] For vehicle operators, understanding the wear-related lifespan of tires fitted to buses, freight vehicles and other vehicles they operate is important for their operations, and there has been a demand to obtain information on predicting tire wear lifespan.

[0003] In view of this, various techniques have been proposed for obtaining prediction information regarding the life span of tires due to wear (Patent Document 1).

[0004] Patent Document 1 discloses a technique for predicting the wear life of a tire when the tire is rolled under predetermined rolling conditions.

[0005] In particular, the above-mentioned conventional technology discloses a method for predicting tire wear life by reflecting severity information (amount of tire friction energy per unit time) that changes depending on driving conditions such as vehicle type, tire mounting position, and driving course.

[0006] This will enable management of the wear life of each tire and the appropriate time to perform tire replacement work to be determined, enabling tire replacement work to be performed efficiently while also ensuring the safety of vehicles such as buses and preventing tire failures. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2008-82914 A Summary of the Invention [Problem to be solved by the invention]

[0008] Incidentally, for buses and freight vehicles operated as route buses or express buses, there is a demand to predict the energy consumption (including so-called fuel efficiency) of these vehicles in addition to predicting the wear life of the tires.

[0009] In other words, for companies that operate vehicles, such as bus companies, obtaining predictive information on energy consumption for each route (for example, fuel consumption for vehicles equipped with internal combustion engines, CO2 emissions, power consumption for electric vehicles, etc.) is extremely important for running their business.

[0010] However, the above-mentioned conventional technology has the disadvantage that it is only capable of predicting tire wear life by reflecting severity information that changes according to driving conditions, and is not capable of predicting energy consumption for each driving route, etc.

[0011] Therefore, the present invention has been made in consideration of the above-mentioned problems, and aims to provide a vehicle energy consumption prediction system, a vehicle energy consumption prediction program, and a prediction method that are capable of predicting energy consumption for a vehicle for each driving route, etc. [Means for solving the problem]

[0012] A vehicle energy consumption prediction system according to one embodiment of the present invention comprises a driving condition acquisition unit that acquires driving conditions including the number of times a vehicle travels on a predetermined driving route and information regarding the wear state of tires fitted to the vehicle, a road surface severity acquisition unit that acquires, for each of the driving routes, a road surface severity equivalent to the energy consumption per distance traveled by the vehicle, and an energy consumption prediction unit that predicts the energy consumption of the vehicle based on the driving conditions and the road surface severity.

[0013] This makes it possible to predict the energy consumption of a vehicle for each travel route, etc.

[0014] The vehicle may further include a road surface severity correction unit that corrects the road surface severity, and the road surface severity correction unit may perform correction using a road surface severity that differs depending on the state of wear of the tires.

[0015] This makes it possible to predict the energy consumption of a vehicle for each travel route with greater accuracy.

[0016] The road surface severity correction unit may correct the road surface severity, which differs depending on the type of the tire, inputted via a predetermined input means.

[0017] This makes it possible to predict with greater accuracy the amount of energy consumption of a vehicle for each driving route, etc., depending on the type of tires.

[0018] The driving conditions may also include information relating to a plurality of wear states of the tires.

[0019] This makes it possible to predict with greater accuracy the amount of energy consumption for a vehicle for each travel route, etc., depending on the state of tire wear.

[0020] The travel condition acquisition unit may set a difference in the number of travels of each of the travel routes within a predetermined range.

[0021] This makes it possible to predict the number of trips along each route so that they are even, based on actual operation.

[0022] Furthermore, the road surface severity acquisition unit can be configured to further acquire road surface severity related to the durability performance of the tires for each of the driving routes, or road surface severity related to the degree of impact on tire durability for each of the driving routes, and can be configured to include a tire durability performance determination unit that determines whether the durability performance or the degree of impact falls within a predetermined threshold value under the driving conditions.

[0023] This makes it possible to predict the energy consumption of a vehicle for each travel route, and to determine the durability performance of tires.

[0024] A vehicle energy consumption prediction program according to another aspect of the present invention includes a driving condition acquisition step for acquiring driving conditions including the number of times a predetermined driving route is traveled by the vehicle and information regarding the wear state of tires fitted to the vehicle, a road surface severity acquisition step for acquiring, for each of the driving routes, a road surface severity equivalent to the energy consumption per distance traveled by the vehicle, and an energy consumption prediction step for predicting the energy consumption of the vehicle based on the driving conditions and the road surface severity, and is executed by an energy consumption prediction system.

[0025] This makes it possible to predict the energy consumption of a vehicle for each travel route, etc.

[0026] In addition, a vehicle energy consumption prediction method according to another aspect of the present invention is summarized as comprising a driving condition acquisition process for acquiring driving conditions including the number of times a predetermined driving route is traveled by the vehicle and information regarding the wear state of tires fitted to the vehicle, a road surface severity acquisition process for acquiring, for each of the driving routes, a road surface severity equivalent to the energy consumption per distance traveled by the vehicle, and an energy consumption prediction process for predicting the energy consumption of the vehicle based on the driving conditions and the road surface severity.

[0027] This makes it possible to predict the energy consumption of a vehicle for each travel route, etc. Effect of the Invention

[0028] According to the present invention, it is possible to provide a vehicle energy consumption prediction system, a vehicle energy consumption prediction program, and a prediction method that are capable of predicting the energy consumption of a vehicle for each travel route, etc. [Brief description of the drawings]

[0029] [Figure 1] 1 is a functional block diagram showing a functional configuration of a vehicle energy consumption prediction system according to an embodiment; [Diagram 2] 10 is a flowchart showing an example of a processing procedure of a vehicle energy consumption prediction process executed in the vehicle energy consumption prediction system according to the embodiment. [Diagram 3] FIG. 2(a) is an explanatory diagram showing a schematic diagram of the change in performance due to differences in the state of tire wear, and FIG. 2(b) is a diagram showing the relationship between the state of tire wear and energy consumption, durability, and wear. [Figure 4] FIG. 1 is an explanatory diagram showing the relationship between the travel route and energy consumption, durability, and wear resistance. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] A vehicle energy consumption prediction system S1 according to an embodiment of the present invention will be described with reference to FIGS. 1 to 4. FIG.

[0031] In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals. However, it should be noted that the drawings are schematic and the ratios of dimensions may differ from those of the actual drawings.

[0032] Therefore, specific dimensions should be determined with reference to the following description. In addition, the drawings may of course include parts with different dimensional relationships and ratios.

[0033] (Outline of vehicle energy consumption prediction system) With reference to the functional block diagram of FIG. 1, a schematic configuration of a vehicle energy consumption prediction system S1 according to an embodiment will be described.

[0034] The vehicle energy consumption prediction system S1 is configured, for example, by a general-purpose computer or server, and is configured to receive various data from a transmitter 200 provided in a vehicle V such as a bus, as shown in FIG.

[0035] As shown in FIG. 1, a vehicle energy consumption prediction system (hereinafter referred to as the energy consumption prediction system) S1 includes a receiving unit 101 that receives various data, such as the number of times a specified route has been traveled and information regarding the wear condition of tires attached to the vehicle V, from a transmitting unit 200 provided in a vehicle V, such as a bus, via a wireless line N.

[0036] The vehicle V may be a vehicle equipped with an internal combustion engine such as a gasoline engine, a diesel engine, or a hydrogen engine, or an electric vehicle.

[0037] The energy consumption prediction system S1 includes a driving condition acquisition unit 102 that acquires driving conditions DC1 including the number of times a vehicle V, such as a bus, travels along a predetermined route R (R1, R2, etc.) and information TW1 on the wear state of tires attached to the vehicle V.

[0038] The energy consumption prediction system S1 also includes a road surface severity acquisition unit 103 that acquires a road surface severity RS1 that corresponds to the energy consumption per travel distance of the vehicle V for each travel route R1, R2, etc.

[0039] The energy consumption per distance traveled by vehicle V can be obtained from actual measurements taken while vehicle V is traveling along each of routes R1, R2, etc.

[0040] The energy consumption prediction system S1 also includes a road surface severity correction unit 104 that performs correction using a road surface severity RS1 that varies depending on the state of tire wear.

[0041] The energy consumption prediction system S1 also includes an energy consumption prediction unit 105 that predicts the energy consumption of the vehicle V based on the driving conditions DC1 and the road surface severity RS1.

[0042] Furthermore, the road surface severity acquisition unit 103 further acquires road surface severity DU1a related to tire durability performance for each driving route R1, R2, etc., or road surface severity DU1b related to the degree of impact on tire durability for each driving route R1, R2, etc., and is provided with a tire durability performance determination unit 106 that determines whether the durability performance or the degree of impact falls within a predetermined threshold value under driving condition DC1.

[0043] The vehicle also includes a display unit 300 configured with a liquid crystal display device or the like for displaying information about the energy consumption predicted by the energy consumption prediction unit 105, the determination results by the tire durability performance determination unit 106, and the like.

[0044] With this configuration, it is possible to predict the energy consumption of vehicle V for each route, etc., thereby improving convenience for bus companies and other operators of buses.

[0045] Further, the road surface severity correction unit 104 can correct the road surface severity RS1 depending on the type of tire.

[0046] The type of tire can be input via an input means (interface) 150 that is configured with a keyboard, a touch panel, or the like.

[0047] This makes it possible to predict energy consumption with greater accuracy depending on the type of tires installed on a vehicle V, such as a bus.

[0048] Furthermore, the driving conditions DC1 can include information on a plurality of tire wear states (for example, early wear, intermediate wear, and final wear, depending on the actual measured value of the tire grooves, etc.).

[0049] This makes it possible to predict with higher accuracy the amount of energy consumption for vehicle V along each of routes R1, R2, etc., depending on the state of tire wear.

[0050] Furthermore, the driving condition acquisition unit 102 can set the difference in the number of times each of the driving routes R1, R2, etc., is driven within a predetermined range. That is, if the driving route R1 is a route that runs through an urban area and the driving route R2 is a route that runs on an expressway, the frequency of driving each of the driving routes can be set to be approximately the same to predict the energy consumption.

[0051] Various information such as the driving conditions DC1 and the road surface severity RS1 may be automatically acquired from the vehicle V side, or may be appropriately inputted from an input means (interface) 150 such as a keyboard.

[0052] (Vehicle energy consumption prediction process) An example of a procedure for vehicle energy consumption prediction processing executed by the vehicle energy consumption prediction system S1 will be described with reference to the flowchart of FIG.

[0053] When this process starts, first, in step S10, it is determined whether or not driving conditions DC1 including information TW1 on the number of times a predetermined route R (R1, R2, etc.) on which a vehicle V such as a bus travels and on the wear state of tires attached to the vehicle V have been acquired.

[0054] The travel conditions can be acquired by setting the difference in the number of travels of each travel route R within a predetermined range.

[0055] If the determination result is "No", the process waits, and if the determination result is "Yes", the process proceeds to step S11.

[0056] In step S11, a road surface severity RS1 corresponding to the energy consumption per travel distance of the vehicle V is obtained, and the process proceeds to step S12.

[0057] In step S12, the road surface severity RS1 is corrected based on information on the tire wear state (for example, early wear, intermediate wear, final wear, etc., depending on the actual measurement value of the tire groove, etc.). At this time, the road surface severity RS1 can be corrected depending on the type of tire.

[0058] Next, in step S13, road surface severity DU1 relating to the durability performance of tires for each travel route R1, R2, etc. is obtained, and the process proceeds to step S14.

[0059] In step S14, it is determined whether the durability performance of the tire falls within a predetermined threshold value.

[0060] If the determination result is "No", the process proceeds to step S15, where a warning is issued, and then the process proceeds to step S16.

[0061] The warning can be given, for example, by displaying a message on the display unit 300 such as "The tire life is approaching the end. Please replace the tire as soon as possible."

[0062] If the determination result is "Yes", the process proceeds to step S16.

[0063] In step S16, the energy consumption of the vehicle V is predicted, and the process ends.

[0064] By performing such processing, it is possible to predict the energy consumption of the vehicle V for each driving route, and also to determine the durability performance of the tires.

[0065] (Regarding the effects of tire wear conditions and driving route combinations, etc.) The effects of the combination of tire wear conditions and driving routes will be described with reference to Figs. 3(a), (b) and 4.

[0066] FIG. 3(a) is an explanatory diagram showing a schematic diagram of the change in performance due to differences in tire wear conditions, and FIG. 3(b) is an example of a chart showing the relationship between tire wear conditions and energy consumption, durability, and wear.

[0067] The upper part of FIG. 3(a) shows a new tire 10a (in the early stages of wear) with deep treads.

[0068] Also, the lower part of FIG. 3(a) shows a tire 10b in a state where the grooves are shallow after the intermediate stage of wear.

[0069] The graph in Figure 3(b) shows the relationship between changes in tire condition performance and energy consumption, durability, and wear.

[0070] In the example shown here, for deep grooves (early wear stage ~), the energy consumption is "poor", the durability is "poor", and the wear characteristics are "same level", while for shallow grooves (mid-stage wear ~), the energy consumption is "good", the durability is "good", and the wear characteristics are "same level".

[0071] FIG. 4 is an explanatory diagram showing the relationship between the travel route and the energy consumption, durability, and wear.

[0072] In the example shown in FIGS. 4(a) and 4(b), operation routes R1 to R3 from point A to point B are assumed as a virtual operation.

[0073] Route R1 will operate only on urban routes, route R2 will use part of the H1a highway, and route R3 will use the H1b highway for approximately half of the route.

[0074] For operation services SE1, SE2, and SE3, a vehicle (such as a bus) V equipped with new to partially worn tires was used, and for operation services SE4, SE5, and SE6, a vehicle (such as a bus) equipped with partially worn to fully worn tires was used to perform virtual operations on each operation route R1 to R3 a predetermined number of times (e.g., about eight times).

[0075] Then, based on the data acquired during such virtual driving, the above-mentioned energy consumption prediction process was executed to predict the energy consumption, etc., of the vehicle V.

[0076] As a result, we obtained the prediction results for energy consumption, durability, and wear resistance, as shown in the lower part of Figure 4(a)-(b). Here, ◎ indicates the best, ○ indicates good, △ indicates average, and × indicates poor.

[0077] Based on this prediction result, for example, the energy consumption effect can be maximized by using deep-tread tire 10a on urban routes (travel route R1) (corresponding to travel service SE1) to preserve durability, and using shallow-tread tire 10b on travel routes R2 and R3 that use expressways H1a and H1b (corresponding to travel services SE2 and SE3).

[0078] The vehicle energy consumption prediction system, tire state quantity estimation program, and tire state quantity estimation method of the present invention have been described above based on the illustrated embodiments, but the present invention is not limited to this, and the configuration of each part can be replaced with any configuration having similar functions.

[0079] The above-mentioned wear state may be predicted to change at a constant rate, or may be predicted in stages (two stages: early wear and final wear).

[0080] In addition, the energy consumption can be predicted for a set travel route based on the actual measured energy consumption.

[0081] Furthermore, the wear conditions of several tires can be calculated as being the same, but if the wear conditions are different, the calculation may be performed by averaging.

[0082] Moreover, the calculation may take into account the effect on performance due to changes in the internal pressure of the tire. [Explanation of symbols]

[0083] V Vehicle S1 Vehicle energy consumption prediction system 101 Receiving unit 102 Driving condition acquisition unit 103 Road surface severity acquisition section 104 Road surface severity correction section 105 Energy Consumption Prediction Department 106 Tire durability performance evaluation section 150 Input means (interface) 300 Display

Claims

1. a driving condition acquisition unit that acquires driving conditions including information on the number of times a vehicle travels along a predetermined driving route and information on the wear state of tires mounted on the vehicle; a road surface severity acquisition unit that acquires a road surface severity corresponding to an energy consumption per travel distance of the vehicle for each of the travel routes; an energy consumption prediction unit that predicts an energy consumption of the vehicle based on the driving conditions and the road surface severity; A vehicle energy consumption prediction system comprising:

2. a road surface severity correction unit that corrects the road surface severity, 2. The system according to claim 1, wherein the road surface severity correction unit performs correction using road surface severity that varies depending on the state of wear of the tires.

3. 3. The vehicle energy consumption prediction system according to claim 2, wherein the road surface severity correction unit corrects the road surface severity, which differs depending on the type of the tire, inputted through a predetermined input means.

4. 4. The vehicle energy consumption prediction system according to claim 1, wherein the driving conditions include information on a plurality of wear states of the tires.

5. 5. The vehicle energy consumption prediction system according to claim 1, wherein the travel condition acquisition unit sets a difference in the number of travels of each of the travel routes within a predetermined range.

6. The road surface severity acquisition unit further acquires a road surface severity related to a durability performance of the tire for each of the travel routes, or a road surface severity related to an influence on tire durability for each of the travel routes, The vehicle energy consumption prediction system according to any one of claims 1 to 5, further comprising a tire durability performance determination unit that determines whether the durability performance or the influence degree falls within a predetermined threshold value under the driving conditions.

7. a driving condition acquisition step of acquiring driving conditions including information on the number of times the vehicle has traveled along a predetermined travel route and the wear state of tires mounted on the vehicle; a road surface severity acquisition step of acquiring a road surface severity corresponding to an energy consumption per travel distance of the vehicle for each of the travel routes; an energy consumption prediction step of predicting an energy consumption of the vehicle based on the driving conditions and the road surface severity; A vehicle energy consumption prediction program, comprising:

8. a driving condition acquisition step of acquiring driving conditions including information on the number of times the vehicle has traveled a predetermined driving route and the wear state of tires mounted on the vehicle; a road surface severity acquisition step of acquiring a road surface severity corresponding to an energy consumption per travel distance of the vehicle for each of the travel routes; an energy consumption prediction step of predicting an energy consumption of the vehicle based on the driving conditions and the road surface severity; A method for predicting energy consumption of a vehicle, comprising:

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