Energy prediction device

Abstract

An energy prediction device (10) is provided with: a pattern storage unit (204) that stores a plurality of vehicle speed patterns as evaluation patterns in association with evaluation traffic information, which is information relating to traffic in which a vehicle travels; a route acquisition unit (102) that acquires a travel route on which the target vehicle whose energy consumption is to be predicted travels; an information acquisition unit (103) that acquires target traffic information relating to traffic on which the target vehicle travels on the travel route; a pattern acquisition unit (104) that acquires an evaluation pattern corresponding to the target traffic information from the plurality of evaluation patterns stored in the pattern storage unit (204); and an energy prediction unit (105) that predicts the energy consumption of the target vehicle traveling on the travel path using the evaluation pattern acquired by the pattern acquisition unit (104).

Description

Technical Field

[0001] The present disclosure relates to an energy prediction device. Background Art

[0002] A technology for predicting the energy consumption of a vehicle is known. For example, a technology for predicting the energy consumption of a vehicle using a mode driving fuel efficiency is disclosed. Specifically, patent document JP-A-2011-53162 discloses a technology for predicting the energy consumption of a vehicle using a mode driving fuel efficiency. According to this technology, the energy consumption amount of each connecting road is calculated in consideration of the driving mode fuel efficiency and other information.

[0003] In the patent document as described above, the user selects the mode driving fuel efficiency as the vehicle characteristic, and the energy consumption is calculated using the selected mode driving fuel efficiency. However, the mode driving fuel efficiency selected by the user may not be an optimized value for calculating the energy consumption. Therefore, any other method may be preferably used to improve the accuracy of predicting the energy consumption. Summary of the invention

[0004] An object of the present disclosure is to improve the accuracy of predicting energy consumption of a vehicle while traveling on a driving path.

[0005] The present disclosure provides an energy prediction device, which includes: a pattern prediction unit (204), which stores multiple vehicle speed patterns as evaluation patterns corresponding to traffic information for evaluation as information related to traffic on which the vehicle is traveling; a path acquisition unit (102), which acquires a travel path on which a target vehicle whose energy consumption is to be predicted is traveled; an information acquisition unit (103), which acquires target traffic information related to traffic on which the target vehicle is traveling on the travel path; a pattern acquisition unit (104), which acquires an evaluation pattern corresponding to the target traffic information from multiple evaluation patterns stored in the pattern storage unit; and an energy prediction unit (105), which uses the evaluation pattern acquired by the pattern acquisition unit to predict the energy consumption of the target vehicle traveling on the travel path.

[0006] According to the present disclosure, the accuracy of predicting the energy consumption of a vehicle traveling on a route can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In the attached picture:

[0008] Figure 1 is a block diagram showing the configuration of an energy prediction device according to the present embodiment;

[0009] Figure 2 It is shown that the use Figure 1A flowchart of the information processing flow of the energy prediction device shown;

[0010] Figure 3 is a table showing an example of the relationship between the average vehicle speed and the evaluation mode;

[0011] Figure 4 is a diagram showing an example of a process of calculating a vehicle speed change pattern using an evaluation pattern;

[0012] Figure 5 is an explanatory diagram showing energy prediction in an electric vehicle;

[0013] Figure 6 is a diagram showing an example of efficiency of an electrical system in energy forecasting;

[0014] Figure 7 is a diagram showing an example of engine efficiency in energy prediction of a vehicle having an internal combustion engine;

[0015] Figure 8 is a diagram showing an example of engine efficiency in energy prediction;

[0016] Fig. 9 is a diagram showing an example of a travel route divided into a plurality of sections;

[0017] Fig.10 is a flowchart showing the flow of information processing when a travel route is divided into a plurality of sections;

[0018] Fig.11 is a diagram showing an example of calculating a vehicle speed change pattern according to an evaluation pattern in a case where a travel route is divided into a plurality of sections;

[0019] Fig.12 is a table showing an example of the relationship between the average vehicle speed and the evaluation mode. DETAILED DESCRIPTION

[0020] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. For ease of description, the same reference numerals are applied to the same components in the accompanying drawings, and redundant descriptions will be omitted.

[0021] Figure 1 1 is an explanatory diagram showing functional components of the energy prediction device 10 according to the present embodiment. Figure 1As shown, the energy prediction device 10 is provided with a vehicle selection unit 101, a path acquisition unit 102, an information acquisition unit 103, a mode acquisition unit 104, an energy prediction unit 105, a vehicle characteristic storage unit 201, a path information storage unit 202, a traffic information storage unit 203 and a mode storage unit 204. The energy prediction device 10 is a computer system provided with a CPU, a storage unit (ROM, RAM, etc.), a communication interface, etc. as a hardware structure. The energy prediction device 10 can be implemented by executing a program stored in a non-temporary tangible recording medium such as a ROM. Note that the energy prediction device 100 can be implemented not only by executing a program (software), but also by a combination of hardware devices (circuits) or a combination of hardware devices and software.

[0022] The vehicle characteristic storage unit 201 is configured to store vehicle characteristic information for calculating energy consumption for each vehicle or each vehicle type. The characteristic information for calculating energy consumption includes vehicle gross weight, air resistance coefficient, frontal projection area, and rolling resistance coefficient.

[0023] The path information storage unit 202 stores information related to the path from any departure point to any destination point. The information related to the path includes information such as distance and inclination.

[0024] The traffic information storage unit 203 stores information related to traffic on the path. The information related to traffic on the path includes characteristic information of vehicle speed on the path, characteristic information of traffic flow on the path, characteristic information on the path, characteristic information of the area around the path, characteristic information of the destination, and characteristic information of weather on the path.

[0025] The characteristic information of vehicle speed on the path includes the average vehicle speed, the average frequency of acceleration and deceleration, and the average number of vehicle stops. The characteristic information of traffic flow on the path includes traffic volume and legal speed limit. The characteristic information of the path includes information such as the number of signals on the path, the number of temporary stops on the path, the average number of lanes on the path, the number of railway crossings on the path, the number of left and right turns on the road, lane control and construction information on the path, the inclination angle on the path, and the curvature of the curved road on the path.

[0026] The characteristic information of the area around the path includes information related to popular stores existing in the area around the path, information related to schools existing in the area around the path, the average age of drivers living in the area around the path, the number of commuters living in the area around the path, and information on whether the area around the path is a business district or a satellite town for commuters. The characteristic information related to the destination includes the number of delivery points when package delivery is required and bus stops included in the path.

[0027] The pattern storage unit 204 stores a plurality of vehicle speed patterns as evaluation patterns in correspondence with evaluation traffic information that is information related to traffic on which the vehicle is traveling. The evaluation traffic information is set in correspondence with the traffic information of the route stored in the traffic information storage unit 203. Thus, the evaluation traffic information includes characteristic information of the vehicle speed on the route, characteristic information of the traffic flow on the route, characteristic information on the route, characteristic information on the area around the route, characteristic information on the destination, and characteristic information on the weather on the route.

[0028] The vehicle selection unit 101 selects a vehicle as a target for predicting energy consumption, and acquires characteristic information related to the selected vehicle from the vehicle characteristic storage unit 201 .

[0029] The route acquisition unit 102 sets the travel route as the target of energy consumption prediction. When there are multiple different routes from the departure point to the destination, the route acquisition unit 102 sets each route as a route candidate and acquires the route information of each route candidate from the route information storage unit 202.

[0030] The information acquisition unit 103 acquires the target traffic information related to the traffic on the route set by the route acquisition unit 102 from the traffic information storage unit 203. The target traffic information acquired by the information acquisition unit 103 may be acquired from a service agency that provides traffic information.

[0031] The pattern acquisition unit 104 acquires an evaluation pattern as a vehicle speed pattern corresponding to the target traffic information acquired by the information acquisition unit 103. The pattern acquisition unit 104 acquires the evaluation pattern corresponding to the target traffic information from among the plurality of evaluation patterns stored in the pattern storage unit 204.

[0032] The energy prediction unit 105 predicts the energy consumption of the target vehicle traveling on the travel route using the evaluation pattern acquired by the pattern acquisition unit 104. The energy prediction unit 105 generates a vehicle speed pattern on the travel route using the evaluation pattern, thereby predicting the energy consumption.

[0033] Then, refer to Figure 2 The flow of information processing by the energy prediction device 10 is described below. In step S001, the vehicle selection unit 101 selects a target vehicle for predicting energy consumption. The target vehicle may be selected in response to a user input operation.

[0034] In step S002 after step S001, the vehicle selection unit 101 sets the vehicle information of the target vehicle. The vehicle information is used to calculate the energy consumption. As the vehicle information, the gross vehicle weight, the air resistance coefficient, the frontal projection area, and the rolling resistance coefficient are included. The vehicle selection unit 101 obtains the vehicle information from the information stored in the vehicle characteristic storage unit 201.

[0035] In step S003 after step S002, the route acquisition unit 102 sets the driving route as an object for predicting energy consumption. In the case where there are multiple driving routes for the destination, the route acquisition unit 102 sets multiple driving routes R(i), where i is a natural number from 1 to n. The route acquisition unit 102 represents each driving route R(i) as L(i).

[0036] In step S004 following step S003, the information acquisition unit 103 acquires object traffic information I(i) related to the traffic of the travel path R(i) set by the information acquisition unit in step S003. The object traffic information I(i) is stored in the traffic information storage unit 203. The object traffic information I(i) may be acquired by a traffic information service provider. As described above, the information related to the traffic of the path includes characteristic information of the vehicle speed on the path, characteristic information of the traffic flow on the path, characteristic information on the path, characteristic information of the area around the path, characteristic information of the destination, and characteristic information of the weather on the path. According to the present embodiment, a case where the object traffic information I(i) is the average vehicle speed v_ave(i) will be described as an example.

[0037] In step S005 following step S004 , the pattern acquisition unit 104 selects the evaluation pattern v_test(i) according to the target traffic information I(i). The evaluation pattern v_test(i) is stored in the pattern storage unit 204 in association with the target traffic information I(i).

[0038] Reference Figure 3 , an example of information stored in the pattern storage unit 204 will be described. Figure 3 In the example shown, the evaluation mode v_test corresponding to the average vehicle speed v_ave is stored. When the average vehicle speed v_ave is "v0", "S0" is selected as the evaluation mode v_test.

[0039] In step S006 following step S005, the energy prediction unit 105 predicts energy consumption when the subject vehicle travels on the travel route R(i). The energy prediction unit 105 generates a vehicle speed pattern for the entire travel route R(i) using the evaluation pattern v_test selected in step S005.

[0040] like Figure 4 As shown in (A), the process repeats the evaluation mode v_test(i) until the entire path of the driving path R(i) is completed. Figure 4 The horizontal axis of (A) represents time, so Figure 4 The horizontal axis shown in (A) is converted to Figure 4The distance axis shown in (B) of FIG. 1 is used to obtain data corresponding to the distance L(i) of the driving path R(i). Figure 4 As shown in (C), when traveling on the driving path R(i) at the generated vehicle speed pattern, the process calculates the inclination information of the driving path R based on the height of the position coordinates. The height information of the position coordinates is stored in the path information storage unit 202. The height information of the position coordinates can be obtained from a service agency that provides the information.

[0041] Next, the calculation process of the energy prediction unit 105 after the generation of the vehicle speed pattern is completed will be described.

[0042] The running resistance Fdrv(t) is calculated using the following formula (f01).

[0043] Fdrv(t)=Wa(t)+0.5*ρ*C d *A V 2(t)+μW g +W g si nθ(t)…(f01),

[0044] Where t = time, W = gross vehicle weight, a(t) = acceleration coefficient at time t, ρ = air density, Cd = air drag coefficient, a = head-on projected area, v(t) = velocity at time t, μ = rolling resistance coefficient, g = acceleration due to gravity, θ(t) = slope between the point at time t and the point at time t-1.

[0045] The air density ρ can be set to a fixed value of 1.293 kg / m 3 The air density ρ can be calculated based on the air temperature. The gravity acceleration g can be a fixed value of 9.8m / s 2 The slope θ(t) can be calculated based on the latitude / longitude information and the altitude information. Note that the information related to the travel route R(i) is stored in the route information storage unit 202. For the vehicle parameters, the information stored in the vehicle characteristic storage unit 201 is read. For example, the following values ​​are used.

[0046] W: 2000kg

[0047] C d : 0.3

[0048] A: 5m 2

[0049] μ: 0.1

[0050] The driving horsepower Pdrv(t) is calculated using the following formula (f02).

[0051] Pdrv(t)=Fdrv(t)*v(t)…(f02)

[0052] Figure 5 An example of a system for an electric vehicle is shown. Figure 5 In the system illustrated in the example, the system efficiency of the electrical system (MG-INV) is expressed as Re l ec, and the system efficiency of the mechanical system is expressed as Rmech. For the mechanical system efficiency Rmech, a fixed value such as 70% can be used. The mechanical system efficiency Rmech indicates that the energy input to the mechanical system is transmitted to the drive wheels with efficiency Rmechs, and the driving horsepower Pdrv(t) is generated. Therefore, the energy input to the mechanical system P'drv(t) is calculated by the following formula (f03).

[0053] P'drv(t)=Pdrv(t) / Rmech...(f03)

[0054] The electrical system efficiency Re l ec is a function of the energy input from the electrical system to the mechanical system, e.g. Figure 6 The electrical system efficiency Re l ec is a function of the energy P'drv(t) input to the mechanical system and is calculated by the following formula (f04).

[0055] The power unit P"drv(t) used for driving is calculated by the following formula (f05).

[0056] P"drv(t)=P'drv(t) / Re l ec(P'drv(t))...(f05)

[0057] When formula (f05) is integrated until time t, the running energy Edrv_prd_base is calculated by the following formula (f106). Note that when P"drv(t) is less than 0 (ie, P"drv(t)<0), the running energy is stored in the battery as regenerative energy.

[0058] Edrv_prd_base=Σ(P”drv(t)*(t-(t-1)))…(f06)

[0059] The power unit of the air conditioner or auxiliary equipment driving source is expressed as Pother(t). Pother(t) can be set to a fixed value such as 5 kW. When Pother(t) is integrated until time t, the driving external energy Eother_prd_base is calculated by the following formula (f07).

[0060] Eother_prd_base=Σ(Pother(t)*(t-(t-1)))…(f07)

[0061] The total energy up to time t using formulas (f06) and (f07) is calculated by the following formula (f08).

[0062] Etota l_prd_base(t)=Edrv_prd_base(t)+Eother_prd_base(t)...(f08)

[0063] The configuration disclosed in the present embodiment can be applied not only to electric vehicles but also to vehicles provided with an internal combustion engine. Figure 7 An example of a system of a vehicle provided with an internal combustion engine is shown. Figure 7 In the system shown, the engine efficiency is represented by Reng and the mechanical efficiency is represented by Rmech. For the mechanical efficiency Rmech, a fixed value such as 70% can be used. The mechanical system efficiency Rmech means that the energy input to the mechanical system is transmitted to the drive wheels with efficiency Rmechs and generates driving horsepower Pdrv(t). Therefore, the energy input to the mechanical system P'drv(t) is calculated by the following formula (f09).

[0064] P'drv(t)=Pdrv(t) / Rmesh...(f09).

[0065] In addition to providing driving energy, the engine also provides energy to drive the air conditioner or auxiliary equipment. The energy required to drive the air conditioner or auxiliary equipment is expressed as Pother(t). Pother(t) can be a fixed value such as 5kW.

[0066] The engine efficiency Reng is a function of the input energy from the engine to the mechanical system and the energy used to drive auxiliary equipment. For example, the engine efficiency Reng is Figure 8 The engine efficiency Reng is a function of P'drv(t)+Pother(t) and is calculated using the following formula (f10).

[0067] Reng=g(P'drv(t)+Pother(t))…(f10)

[0068] By using formula (f09) and formula (f10), the total energy is calculated using the following formulas (f11) and (f12).

[0069] Psum(t)=P'drv(t)+Pother(t)...(f11)

[0070] P'sum(t)=Psum(t) / Reng(Psum(t))…(f12)

[0071] Since a vehicle provided with an internal combustion engine does not utilize regenerative energy, only positive values ​​are considered.

[0072] P”sum(t)=P’sum(t)(P’sum(t)>0)…(f13)

[0073] P"sum(t) is integrated over time, and the required energy Etotal_pd_base is calculated using the following formula (f14).

[0074] In the above description, the driving route is defined as one road section. However, the driving route may be divided into a plurality of road sections, and energy consumption may be predicted using information related to traffic applicable to the divided road sections.

[0075] exist Fig. 9 In the example shown, the driving path is divided into road section i, road section j and road section k. The distance and average driving speed in each road section are: distance Li and average driving speed vi of road section i; distance Lj and average driving speed vj of road section j; and distance Lk and average driving speed vk of road section k.

[0076] Reference Fig.10 , the information processing flow processed by the energy prediction device 10 in the case where the driving path is divided into sections will be described. In step S101, the vehicle selection unit 101 selects a target vehicle for predicting energy consumption. The target vehicle may be selected according to a user input.

[0077] In step S102 following step S101, the vehicle selection unit 101 sets the vehicle information of the target vehicle. The vehicle information is used when calculating the energy consumption. The vehicle information includes the gross vehicle weight, the air resistance coefficient, the frontal projection area, and the rolling resistance coefficient. The vehicle selection unit 101 acquires the vehicle information from the information stored in the vehicle characteristic storage unit 201.

[0078] In step S103 after step S102, the route acquisition unit 102 sets the driving route as an object for predicting energy consumption. In the case where it is assumed that there are multiple driving routes for the destination, the route acquisition unit 102 sets multiple driving routes. Note that in this description, one driving route is set so that the divided sections are explained in detail. The route acquisition unit 102 divides the driving route into section i, section j, and section k.

[0079] In step S104 after step S103, the information acquisition unit 103 acquires the object traffic information related to the driving path set in step S103. The object traffic information is stored in the traffic information storage unit 203. The object traffic information can be acquired by a traffic information service provider. As described above, the information related to the traffic of the path includes characteristic information of the vehicle speed on the path, characteristic information of the traffic flow on the path, characteristic information on the path, characteristic information of the area around the path, characteristic information of the destination, and characteristic information of the weather on the path. In the description of this embodiment, the case where the object traffic information is the average vehicle speed will be exemplified.

[0080] like Fig. 9 As shown, the driving path is divided into road section i, road section j and road section k. The distance and average driving speed in each road section are: the distance Li and average driving speed vi of road section i; the distance Lj and average driving speed vj of road section j; and the distance Lk and average driving speed vk of road section k.

[0081] In step S105 following step S104, the pattern acquisition unit 104 selects the evaluation pattern v_test(i) based on the target traffic information. The evaluation pattern v_test is stored in the pattern storage unit 204 corresponding to the target traffic information.

[0082] As reference Figure 3 As described, the evaluation mode v_test is stored corresponding to the average vehicle speed v_ave. When the average vehicle speed v_ave is "v0", "S0" is selected as the evaluation mode v_test. Therefore, according to this example, the evaluation mode v_test corresponding to the average vehicle speeds vi, vj, vk is selected. Note that the evaluation modes corresponding to the average vehicle speeds vi, vj, vk are represented as evaluation modes v_test_i, v_test_j, and v_test_k, respectively.

[0083] In step S106 following step S105, the energy prediction unit 105 predicts energy consumption when the subject vehicle travels on the travel route. The energy prediction unit 104 uses the evaluation patterns v_test_i, v_test_j, and v_test_k selected in step S005 to generate a vehicle speed pattern for the entire travel route.

[0084] like Fig.11As shown in (A), the process repeats the evaluation patterns v_test_i, v_test_j, v_test_k until the entire driving path is completed. More specifically, the process repeats the evaluation pattern v_test_i until the entire segment i of the driving path is completed. Similarly, the process repeats the evaluation pattern v_test_j until the entire segment j of the driving path is completed, and repeats the evaluation pattern v_test_k until the entire segment k is completed.

[0085] because Fig.11 The horizontal axis of (A) is the time axis, so convert the horizontal axis to Fig.11 The distance axis shown in (B) is used to match the data with the distances L i, L j and L k of the road segments i, j and k. Fig.11 As shown in (C), the process calculates the inclination information of the road sections i, j and k based on the height of the position coordinates when traveling on the road sections i, j and k with the generated vehicle speed pattern. The height information of the position coordinates is stored in the path information storage unit 202. The height information of the position coordinates can be obtained from the service agency that provides the information.

[0086] Since the calculation process performed by the energy prediction unit 105 after generating the vehicle speed pattern is the same as that of the reference Figures 5 to 8 The calculation process described is the same, so its description will be omitted.

[0087] In the above description, the case where the traffic information corresponding to the evaluation mode v_test is the average vehicle speed v_ave is described. As described above, the traffic information is not limited to the average vehicle speed, but may include characteristic information of the vehicle speed on the path, characteristic information of the traffic flow on the path, characteristic information on the path, characteristic information of the area around the path, characteristic information of the destination, and characteristic information of the weather on the path.

[0088] The characteristic information of vehicle speed on the path includes the average vehicle speed, the average frequency of acceleration and deceleration, and the average number of vehicle stops. The characteristic information of traffic flow on the path includes traffic volume and legal speed limit. The characteristic information of the path includes information such as the number of signals on the path, the number of temporary stops on the path, the average number of lanes on the path, the number of railway crossings on the path, the number of left and right turns on the road, lane control and construction information on the path, the inclination angle on the path, and the curvature of the curved road on the path.

[0089] The characteristic information of the area around the path includes information related to popular stores existing in the area around the path, information related to schools existing in the area around the path, the average age of drivers living in the area around the path, the number of commuters living in the area around the path, and information on whether the area around the path is a business district or a satellite town for commuters. The characteristic information related to the destination includes the number of delivery points when package delivery is required and bus stops included in the path.

[0090] like Fig.12 As shown, the evaluation mode v_test may be stored corresponding to the average vehicle speed and the number of signals. When the average vehicle speed v_ave is "v0" and the number of signals is "to 10", "S0" is selected as the evaluation mode v_test.

[0091] [appendix]

[0092] The following Appendices 1 to 3 may be combined arbitrarily as long as no technical inconsistency occurs.

[0093] [Appendix 1]

[0094] An energy prediction device (10), comprising:

[0095] a pattern storage unit (204) storing a plurality of vehicle speed patterns as evaluation patterns in correspondence with traffic information for evaluation, which is information related to traffic on which the vehicle is traveling;

[0096] A path acquisition unit (102), wherein the path acquisition unit acquires a travel path of a target vehicle whose energy consumption is to be predicted;

[0097] An information acquisition unit (103), the information acquisition unit acquiring target traffic information related to the traffic of the target vehicle traveling on the driving path;

[0098] a pattern acquisition unit (104) for acquiring an evaluation pattern corresponding to the target traffic information from a plurality of evaluation patterns stored in a pattern storage unit (204); and

[0099] An energy prediction unit (105) predicts energy consumption of the target vehicle traveling on the travel route using the evaluation pattern acquired by the pattern acquisition unit (104).

[0100] According to Appendix 1, since an evaluation pattern corresponding to the target traffic information, which is information related to the traffic on which the target vehicle travels on the travel path, is acquired, a vehicle speed pattern depending on the traffic state of the travel path can be selected, so that the accuracy of predicting energy consumption can be improved. The pre-stored evaluation pattern is used to generate a vehicle speed pattern on the travel path to predict energy consumption. Therefore, calculation can be performed efficiently while improving the accuracy of predicting energy consumption, and the calculation speed can be increased.

[0101] According to the description of the present embodiment, when an evaluation pattern is used to generate a vehicle speed pattern on a driving path, an example is described in which the evaluation pattern is repeated until the entire driving path is completed. In addition, it is illustrated that since the horizontal line of the evaluation pattern is a time axis, the horizontal axis is converted into a distance axis so that the data matches the distance of the driving path. However, the method of using an evaluation pattern to generate a vehicle speed pattern on a driving path is not limited to the above method. For example, when the evaluation pattern corresponds to a sufficiently long time period, the evaluation pattern may not be repeated, but a part of the evaluation pattern may be used for the vehicle speed pattern on the driving path. For example, an evaluation pattern in which the horizontal axis is a distance axis may be prepared in advance.

[0102] [Appendix 2]

[0103] The energy prediction device (10) according to Appendix 1, wherein:

[0104] The route acquisition unit (102) divides the travel road into a plurality of road sections;

[0105] The information acquisition unit (104) acquires the target traffic information of each road section; the mode acquisition unit acquires the evaluation mode corresponding to the target traffic information of each road section,

[0106] The energy prediction unit (105) predicts the energy consumption of the target vehicle traveling on the travel route using the evaluation pattern of each road section.

[0107] According to Appendix 2, the driving road is divided into a plurality of sections, the object traffic information of each section is obtained to select an evaluation mode, and the evaluation mode of each section is used to predict energy consumption. Therefore, the accuracy of predicting energy consumption can be improved. In addition, in Appendix 2, a vehicle speed pattern on the driving path is generated using a pre-stored evaluation mode, and energy consumption is predicted. Therefore, calculations can be performed efficiently while improving the accuracy of predicting energy consumption, and the calculation speed can be increased.

[0108] [Appendix 3]

[0109] The energy prediction device according to Appendix 1 or 2, wherein:

[0110] The above-mentioned evaluation traffic information and the above-mentioned object traffic information include at least one of characteristic information of the above-mentioned vehicle speed, characteristic information of traffic flow, characteristic information related to the above-mentioned path, characteristic information of the area around the above-mentioned path, characteristic information of the destination and characteristic information of weather.

[0111] As mentioned above, the present embodiment is described with reference to specific examples. However, the present disclosure is not limited to these specific examples. For these specific examples, those of ordinary skill in the art can appropriately modify their design. As long as the features of the present disclosure are provided therein, the design of these modifications is included in the scope of the present disclosure. In addition, the various elements, their arrangement, conditions and shapes included in the specific examples as described above are not limited to the elements of the examples as described above, and can be appropriately modified. As long as there is no technical inconsistency, the various elements in the specific examples as described above can be appropriately combined.

[0112] The energy prediction device and method disclosed in the present disclosure can be implemented by a special-purpose computer composed of a processor and a memory, and the special-purpose computer is programmed to perform one or more functions implemented by a computer program. Alternatively, the energy prediction device and method disclosed in the present disclosure can be implemented by a special-purpose computer provided by a processor composed of one or more special-purpose hardware logic circuits. In addition, the energy prediction device and method disclosed in the present disclosure can be implemented by one or more special-purpose computers, which are composed of a processor and a memory programmed to perform one or more functions combined with a processor composed of one or more hardware logic circuits. In addition, the computer program can be stored in a computer-readable, non-transitory, tangible recording medium as an instruction code to be executed by a computer.

[0113] (in conclusion)

[0114] The present disclosure provides an energy prediction device, which includes: a pattern prediction unit (204), which stores a plurality of vehicle speed patterns as evaluation patterns corresponding to traffic information for evaluation as information related to traffic on which the vehicle is traveling; a path acquisition unit (102), which acquires a travel path on which a target vehicle whose energy consumption is to be predicted is traveled; an information acquisition unit (103), which acquires target traffic information related to traffic on which the target vehicle is traveling on the travel path; a pattern acquisition unit (104), which acquires an evaluation pattern corresponding to the target traffic information from a plurality of evaluation patterns stored in the pattern storage unit; and an energy prediction unit (105), which uses the evaluation pattern acquired by the pattern acquisition unit to predict the energy consumption of the target vehicle traveling on the travel path.

[0115] According to the present disclosure, the accuracy of predicting the energy consumption of a vehicle traveling on a route can be improved.

Claims

1. An energy prediction device, comprising: a pattern storage unit storing a plurality of vehicle speed patterns as evaluation patterns in correspondence with evaluation traffic information that is information related to traffic on which the vehicle is traveling; a path acquisition unit, the path acquisition unit acquiring a travel path of a target vehicle whose energy consumption is to be predicted; an information acquisition unit, the information acquisition unit acquiring target traffic information related to traffic of the target vehicle traveling on the driving path; a mode acquisition unit, the mode acquisition unit acquiring an evaluation mode corresponding to the target traffic information from a plurality of evaluation modes stored in the mode storage unit; as well as An energy prediction unit predicts energy consumption of the subject vehicle traveling on the travel route using the evaluation pattern acquired by the pattern acquisition unit.

2. The energy prediction device according to claim 1, characterized in that: The route acquisition unit divides the travel road into a plurality of sections. The information acquisition unit acquires the target traffic information of each road section. The mode acquisition unit acquires an evaluation mode corresponding to the target traffic information of each road section, The energy prediction unit predicts energy consumption of the subject vehicle traveling on the travel route using the evaluation pattern for each road section.

3. The energy prediction device according to claim 1 or 2, characterized in that: The evaluation traffic information and the target traffic information include at least one of characteristic information of the vehicle speed, characteristic information of traffic flow, characteristic information related to the route, characteristic information of an area around the route, characteristic information of a destination, and characteristic information of weather.

4. A method for predicting energy consumption performed by a computer, comprising: storing a plurality of vehicle speed patterns as evaluation patterns in correspondence with evaluation traffic information that is information related to traffic on which the vehicle travels; Obtaining a driving path of a target vehicle whose energy consumption is to be predicted; acquiring object traffic information related to traffic of the object vehicle traveling on the driving path; acquiring an evaluation mode corresponding to the object traffic information from the stored plurality of evaluation modes; as well as The acquired evaluation pattern is used to predict energy consumption of the subject vehicle traveling on the travel path.

5. A program stored in a non-transitory tangible recording medium readable by a computer, the program causing a computer to execute a method for predicting energy consumption, the method comprising: storing a plurality of vehicle speed patterns as evaluation patterns in correspondence with evaluation traffic information that is information related to traffic on which the vehicle travels; Obtaining a driving path of a target vehicle whose energy consumption is to be predicted; acquiring object traffic information related to traffic of the object vehicle traveling on the driving path; acquiring an evaluation mode corresponding to the object traffic information from the stored plurality of evaluation modes; as well as The acquired evaluation pattern is used to predict energy consumption of the subject vehicle traveling on the travel path.

Images