Energy prediction device

Abstract

To increase the accuracy of predicting energy consumption when a vehicle travels on a route.SOLUTION: An energy prediction device 10 comprises: a route acquisition unit 102 that acquires a travel route on which a target vehicle travels for which energy consumption is predicted; an information acquisition unit 103 that acquires target traffic information that is information on the traffic when the target vehicle travels on the travel route; a pattern acquisition unit 104 that acquires, from a plurality of evaluation patterns stored in a pattern storage unit 204, an evaluation pattern corresponding to the target traffic information; and an energy prediction unit 105 that predicts the energy consumption when the target vehicle travels on the travel route by using the evaluation pattern acquired by the pattern acquisition unit 104.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] The following Patent Document 1 discloses predicting the energy consumption of a vehicle using a test cycle fuel efficiency. In the following Patent Document 1, the energy consumption for each road link is calculated taking into account the test cycle fuel efficiency and other information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-53162 A Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, a user selects a mode driving fuel efficiency as a 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 is not necessarily optimal for calculating the energy consumption, and there is room for improving the prediction accuracy of the energy consumption by other means.

[0005] The present disclosure aims to improve the accuracy of predicting energy consumption when a vehicle travels along a route. [Means for solving the problem]

[0006] The present disclosure provides an energy prediction device that includes a pattern storage unit (204) that stores a plurality of vehicle speed patterns as evaluation patterns corresponding to evaluation traffic information, which is information regarding traffic when a vehicle travels, a route acquisition unit (102) that acquires a driving route along which a target vehicle for which energy consumption is to be predicted travels, an information acquisition unit (103) that acquires target traffic information, which is information regarding traffic when the target vehicle travels along the driving 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, and an energy prediction unit (105) that predicts energy consumption when the target vehicle travels along the driving route using the evaluation pattern acquired by the pattern acquisition unit. Effect of the Invention

[0007] According to the present disclosure, it is possible to improve the accuracy of predicting energy consumption when a vehicle travels along a route. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram for explaining the energy prediction device according to this embodiment. [Diagram 2] FIG. 2 is a flowchart for explaining an information processing flow using the energy prediction device shown in FIG. [Diagram 3] FIG. 3 is a diagram showing an example of a correspondence relationship between the average vehicle speed and the evaluation pattern. [Figure 4] FIG. 4 is a diagram showing an example of calculating a vehicle speed fluctuation pattern from an evaluation pattern. [Diagram 5] FIG. 5 is a diagram for explaining energy estimation in an electric vehicle. [Figure 6] FIG. 6 is a diagram showing an example of electrical system efficiency in energy estimation. [Figure 7] FIG. 7 is a diagram showing an example of engine efficiency in energy estimation in an engine vehicle. [Figure 8]FIG. 8 is a diagram showing an example of engine efficiency in energy estimation. [Figure 9] FIG. 9 is a diagram for explaining an example of dividing a travel route into a plurality of sections. [Figure 10] FIG. 10 is a flowchart for explaining the information processing flow when a travel route is divided into a plurality of sections. [Figure 11] FIG. 11 is a diagram showing an example of calculating a vehicle speed fluctuation pattern from an evaluation pattern when a travel route is divided into a plurality of sections. [Figure 12] FIG. 12 is a diagram showing an example of the correspondence relationship between the average vehicle speed and the evaluation pattern. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the present embodiment will be described with reference to the accompanying drawings. In order to facilitate understanding of the description, the same components in each drawing are denoted by the same reference numerals as much as possible, and duplicated description will be omitted.

[0010] Fig. 1 is a diagram for explaining functional components of an energy prediction device 10 in this embodiment. As shown in Fig. 1, the energy prediction device 10 includes a vehicle selection unit 101, a route acquisition unit 102, an information acquisition unit 103, a pattern acquisition unit 104, an energy prediction unit 105, a vehicle characteristic storage unit 201, a route information storage unit 202, a traffic information storage unit 203, and a pattern storage unit 204. The energy prediction device 10 is a computer system having a CPU, a memory, a communication interface, etc., as a hardware configuration.

[0011] The vehicle characteristic storage unit 201 is a part that stores vehicle characteristic information for each vehicle or each model used when calculating the energy consumption. The characteristic information used when calculating the energy consumption includes the total vehicle weight, the air resistance coefficient, the frontal projection area, and the rolling resistance coefficient.

[0012] The route information storage unit 202 is a section for storing information about a route from a certain starting point to a certain destination point. The information about the route includes information about distance and information about slope.

[0013] The traffic information storage unit 203 is a part that stores information related to traffic on the route. The information related to traffic on the route includes characteristic information on vehicle speed on the route, characteristic information on traffic flow on the route, characteristic information on the route, characteristic information on the surrounding area of ​​the route, characteristic information on the destination, and characteristic information on weather on the route.

[0014] The characteristic information of the vehicle speed on the route includes the average vehicle speed, the average frequency of acceleration / deceleration, and the average number of stops. The characteristic information of the traffic flow on the route includes the traffic flow rate and the legal vehicle speed. The characteristic information of the route includes information such as the number of traffic lights on the route, the number of stop signs on the route, the average number of lanes on the route, the number of railroad crossings on the route, the number of right and left turns on the route, information on lane restrictions and construction on the route, the slope along the route, and the curvature of curves along the route.

[0015] The characteristic information of the area around the route includes information on popular stores around the route, information on schools around the route, the average age of drivers living around the route, the number of commuters living around the route, and information on whether the area around the route is a business district or a commuter town. The characteristic information of the destination includes information on the number of delivery points and the number of bus stops on the route in the case of a parcel delivery service.

[0016] The pattern storage unit 204 is a part that stores, as evaluation patterns, a plurality of vehicle speed patterns corresponding to the evaluation traffic information, which is information about the traffic when the vehicle travels. The evaluation traffic information is set so as to correspond to the information about the traffic on the route stored in the traffic information storage unit 203. Therefore, the evaluation traffic information includes characteristic information about the vehicle speed on the route, characteristic information about the traffic flow on the route, characteristic information about the route, characteristic information about the surrounding area of ​​the route, characteristic information about the destination, and characteristic information about the weather on the route.

[0017] The vehicle selection unit 101 is a part that selects a vehicle to be subjected to energy consumption prediction, and acquires characteristic information on the selected vehicle from the vehicle characteristic storage unit 201.

[0018] The route acquisition unit 102 is a part that sets a travel route to be the target of energy consumption prediction. If there are multiple routes from a starting point to a destination point, the route acquisition unit 102 sets each route as a route candidate, and acquires information about the route for each route candidate from the route information storage unit 202.

[0019] The information acquisition unit 103 is a part that acquires target traffic information, which is information about traffic on the route set by the route acquisition unit 102, from the traffic information storage unit 203. The traffic information acquired by the information acquisition unit 103 may be obtained from a service facility that provides traffic information.

[0020] The pattern acquisition unit 104 is a part that acquires an evaluation pattern, which is a vehicle speed pattern corresponding to the target traffic information acquired by the information acquisition unit 103. The pattern acquisition unit 104 acquires an evaluation pattern corresponding to the target traffic information from a plurality of evaluation patterns stored in the pattern storage unit 204.

[0021] The energy prediction unit 105 is a part that predicts the energy consumption when the target vehicle travels along a travel route by using the evaluation pattern acquired by the pattern acquisition unit 104. The energy prediction unit 105 creates a vehicle speed pattern along the travel route by using the evaluation pattern, and predicts the energy consumption.

[0022] Next, an information processing flow using the energy prediction device 10 will be described with reference to Fig. 2. In step S001, the vehicle selection unit 101 selects a target vehicle for which energy consumption is to be predicted. The selection of the target vehicle may be performed according to an input from a user.

[0023] In step S002 following step S001, the vehicle selection unit 101 sets vehicle information of the target vehicle. The vehicle information is information used when calculating the energy consumption. The vehicle information includes the total vehicle weight, the air resistance coefficient, the frontal projection area, the rolling resistance coefficient, etc. The vehicle selection unit 101 acquires the vehicle information from the information stored in the vehicle characteristics storage unit 201.

[0024] In step S003 following step S002, the route acquisition unit 102 sets a travel route to be the target of energy consumption prediction. When multiple travel routes to the destination are assumed, the route acquisition unit 102 sets multiple travel routes R(i), where i is a natural number from 1 to n. The route acquisition unit 102 sets the distance of each travel route R(i) as L(i).

[0025] In step S004 following step S003, the information acquisition unit 103 acquires target traffic information I(i) related to traffic on the travel route R(i) set in step S003. The target traffic information I(i) is stored in the traffic information storage unit 203. The target traffic information I(i) may be acquired from a traffic service provider. As described above, the information related to traffic on the route includes characteristic information of vehicle speed on the route, characteristic information of traffic flow on the route, characteristic information of the route, characteristic information of the surrounding area of ​​the route, characteristic information of the destination, and characteristic information of weather on the route. In the description of this embodiment, a case where the target traffic information I(i) is the average vehicle speed v_ave(i) will be described as an example.

[0026] In step S005 following step S004, the pattern acquisition unit 104 selects an evaluation pattern v_test(i) corresponding 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).

[0027] An example of information stored in the pattern storage unit 204 will be described with reference to Fig. 3. In the example shown in Fig. 3, an evaluation pattern v_test is stored in correspondence with an average vehicle speed v_ave. If the average vehicle speed v_ave is "v0", "S0" is selected as the evaluation pattern v_test.

[0028] In step S006 following step S005, the energy prediction unit 105 predicts the energy consumption when the target vehicle travels along 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(i) selected in step S005.

[0029] As shown in Fig. 5(A), the evaluation pattern v_test(i) is repeated until the entire travel route R(i) is covered. Since the horizontal axis in Fig. 5(A) is the time axis, it is converted so that the horizontal axis becomes the distance axis as shown in Fig. 5(B), and data is made to match the distance L(i) of the travel route R(i). As shown in Fig. 5(C), slope information of the travel route R(i) is generated from the altitude in the position coordinates when moving along the travel route R(i) with the generated vehicle speed pattern. The information on the altitude in the position coordinates is stored in the route information storage unit 202. The information on the altitude in the position coordinates may be obtained from a service facility that provides such information.

[0030] Next, the calculations performed by the energy prediction unit 105 after the vehicle speed pattern is generated will be described.

[0031] Running resistance F drv (t) is calculated using the following equation (f01). F drv (t)=Wa(t)+0.5*ρ*Cd*Av 2 (t)+μWg+Wgsinθ(t) ···(f01) t: time W: Total vehicle weight a(t): Acceleration at time t ρ: air density Cd: Air resistance coefficient A: Front projected area v(t): velocity at time t μ: rolling resistance coefficient g:Gravity acceleration θ(t): Gradient between a point at time t and a point at time t-1

[0032] The air density ρ is a fixed value of 1.293 kg / m 3 The air density ρ may be calculated from the air temperature. The gravitational acceleration g is a fixed value of 9.8 m / s 2 The gradient θ(t) can be calculated from the latitude and longitude information and altitude information of the travel route R(i). Such information about the travel route R(i) is stored in the route information storage unit 202. The vehicle parameters are read from the information stored in the vehicle characteristics storage unit 201. For example, the following values ​​are used: W: 2,000kg Cd:0.3 A: 5m 2 μ:0.1

[0033] Running horsepower P drv (t) is calculated using the following equation (f02). P drv (t)=F drv (t)*v(t) (f02)

[0034] Figure 5 shows an example of a system for an electric vehicle. In the system shown in Figure 5, the system efficiency of the electric system (MG-INV) is calculated as R elec Let the efficiency of the mechanical system be R mech The efficiency of the mechanical system R mech can be a fixed value such as 70%. The efficiency of the mechanical system R mech is the energy input to the mechanical system with efficiency R mech is transmitted to the drive wheels, and the running horsepower P drv Therefore, the energy P´ input to the mechanical system is drv (t) can be calculated using the following equation (f03). P´ drv (t)=P drv(t) / R mech (f03)

[0035] Electrical system efficiency R elec is a function of the energy input from the electrical system to the mechanical system, and is determined, for example, as shown in Figure 6. The efficiency of the electrical system R elec is the energy input to the mechanical system P´ drv It is a function of (t) and can be calculated using the following equation (f04). R elec =f(P´ drv (t)) ···(f04)

[0036] Power for driving P´´ drv (t) can be calculated using the following equation (f05). P´´ drv (t)=P´ drv (t) / R elec (P´ drv (t)) ···(f05)

[0037] If we integrate equation (f05) up to time t, we get the driving energy E drv_prd_base can be calculated using the following formula (f06). drv When (t)<0, the energy is stored in the battery as regenerative energy. E drv_prd_base =Σ(P´´ drv (t)*(t―(t-1))) (f06)

[0038] The power for driving the air conditioner and auxiliary equipment is P other Let (t). P other (t) may be a fixed value such as 5kW. other (t) is integrated up to time t, the non-driving energy E other_prd_base can be calculated using the following equation (f07). E other_prd_base =Σ(P other (t)*(t―(t-1))) (f07)

[0039] From equations (f06) and (f07), the total amount of energy up to time t can be calculated using the following equation (f08). E total_prd_base (t)=E drv_prd_base (t)+E other_prd_base (t) (f08)

[0040] This embodiment can be applied not only to electric vehicles but also to engine vehicles. FIG. 7 shows an example of a system for an engine vehicle. In the system shown in FIG. 7, the engine efficiency is calculated as R eng Let the efficiency of the mechanical system be R mech The efficiency of the mechanical system R mech can be a fixed value such as 70%. The efficiency of the mechanical system R mech is the energy input to the mechanical system with efficiency R mech is transmitted to the drive wheels, and the running horsepower P drv Therefore, the energy P´ input to the mechanical system is drv (t) can be calculated using the following equation (f09). P´ drv (t)=P drv (t) / R mech (f09)

[0041] In addition to the energy required for running the vehicle, the engine also supplies energy to run the air conditioner and auxiliary equipment. The energy required to run the air conditioner and auxiliary equipment is called P other Let (t). P other (t) may be a fixed value such as 5 kW.

[0042] Engine efficiency R eng is a function of the energy input from the engine to the mechanical system and the energy for driving the auxiliary equipment, and is determined, for example, as shown in FIG. eng is P´ drv (t)+P other It is a function of (t) and can be calculated using the following equation (f10). R eng =g(P´ drv (t)+P other(t)) ···(f10)

[0043] Equations (f09) and (f10) are added together to predict the total amount of energy using the following equations (f11) and (f12). P sum (t)=P´ drv (t)+P other (t) (f11) P´ sum (t)=P sum (t) / R eng (P sum (t)) ···(f12)

[0044] Since engine vehicles do not store regenerative energy, only positive values ​​are considered. P´´ sum (t)=P´ sum (t)(P´ sum (t)>0) (f13)

[0045] P´´ sum (t) is integrated over time and the required energy amount E total_prd_base is calculated using the following equation (f14). E total_prd_base (t)=Σ(P´´ sum (t)*(t―(t-1))) (f14)

[0046] In the above explanation, the travel route is treated as one section, but the travel route may be divided into sections, and energy consumption may be predicted using information about traffic suited to each divided section.

[0047] In the example shown in Fig. 9, the travel route is divided into sections i, j, and k. Section i has a distance Li and an average vehicle speed vi. Section j has a distance Lj and an average vehicle speed vj. Section k has a distance Lk and an average vehicle speed vk.

[0048] An information processing flow using the energy prediction device 10 in the case of section division will be described with reference to Fig. 10. In step S101, the vehicle selection unit 101 selects a target vehicle for which energy consumption is predicted. The selection of the target vehicle may be performed according to an input from a user.

[0049] In step S102 following step S101, the vehicle selection unit 101 sets vehicle information of the target vehicle. The vehicle information is information used when calculating the energy consumption. The vehicle information includes the total vehicle weight, the air resistance coefficient, the frontal projection area, the rolling resistance coefficient, etc. The vehicle selection unit 101 acquires the vehicle information from the information stored in the vehicle characteristics storage unit 201.

[0050] In step S103 following step S102, the route acquisition unit 102 sets a travel route to be the target of energy consumption prediction. When multiple travel routes to the destination are assumed, the route acquisition unit 102 sets multiple travel routes. In this explanation, in order to focus on the explanation of section division, it is assumed that one travel route is set. The route acquisition unit 102 divides the travel route into section i, section j, and section k.

[0051] In step S104 following step S103, the information acquisition unit 103 acquires target traffic information related to traffic on the travel route set in step S103. The target traffic information is stored in the traffic information storage unit 203. The target traffic information may be acquired from a traffic service provider. As described above, the information related to traffic on the route includes characteristic information of vehicle speed on the route, characteristic information of traffic flow on the route, characteristic information of the route, characteristic information of the surrounding area of ​​the route, characteristic information of the destination, and characteristic information of weather on the route. In the description of this embodiment, a case where the target traffic information is an average vehicle speed v will be described as an example.

[0052] As described with reference to Fig. 9, the travel route is divided into sections i, j, and k. Section i has a distance Li and an average vehicle speed vi. Section j has a distance Lj and an average vehicle speed vj. Section k has a distance Lk and an average vehicle speed vk.

[0053] In step S105 following step S104, the pattern acquisition unit 104 selects an evaluation pattern v_test corresponding to the target traffic information. The evaluation pattern v_test is stored in the pattern storage unit 204 in association with the target traffic information.

[0054] As described with reference to FIG. 3, the evaluation pattern v_test is stored in correspondence with the average vehicle speed v_ave. If the average vehicle speed v_ave is "v0", the evaluation pattern v_test is selected as "S0". Therefore, in this example, the evaluation pattern v_test corresponding to the average vehicle speeds vi, vj, and vk is selected. The evaluation patterns corresponding to the average vehicle speeds vi, vj, and vk are designated as evaluation patterns v_test_i, v_test_j, and v_test_k.

[0055] In step S106 following step S105, the energy prediction unit 105 predicts the energy consumption when the target vehicle travels along the travel route. The energy prediction unit 105 generates a vehicle speed pattern over the entire travel route by using the evaluation patterns v_test_i, v_test_j, and v_test_k selected in step S005.

[0056] As shown in Fig. 11(A), evaluation patterns v_test_i, v_test_j, and v_test_k are repeated until the entire travel route is covered. More specifically, evaluation pattern v_test_i is repeated until the entire section i is covered. Evaluation pattern v_test_j is repeated until the entire section j is covered. Evaluation pattern v_test_k is repeated until the entire section k is covered.

[0057] Since the horizontal axis in Fig. 11(A) is the time axis, it is converted so that the horizontal axis becomes the distance axis as shown in Fig. 11(B), and data is made to match the distances Li, Lj, and Lk of sections i, j, and k. As shown in Fig. 11(C), slope information for sections i, j, and k is generated from the altitude at the position coordinates when moving through sections i, j, and k with the generated vehicle speed pattern. Information on the altitude at the position coordinates is stored in the route information storage unit 202. Information on the altitude at the position coordinates may be obtained from a service facility that provides such information.

[0058] The calculations of the energy prediction unit 105 after the vehicle speed pattern is generated are similar to those described with reference to Figs. 5 to 8, and therefore will not be described here.

[0059] In the above description, the traffic information associated with the evaluation pattern v_test is the average vehicle speed v_ave. As described above, the traffic information is not limited to the average vehicle speed, but includes characteristic information of the vehicle speed on the route, characteristic information of the traffic flow on the route, characteristic information of the route, characteristic information of the surrounding area of ​​the route, characteristic information of the destination, and characteristic information of the weather on the route.

[0060] The characteristic information of the vehicle speed on the route includes the average vehicle speed, the average frequency of acceleration / deceleration, and the average number of stops. The characteristic information of the traffic flow on the route includes the traffic flow rate and the legal vehicle speed. The characteristic information of the route includes information such as the number of traffic lights on the route, the number of stop signs on the route, the average number of lanes on the route, the number of railroad crossings on the route, the number of right and left turns on the route, information on lane restrictions and construction on the route, the slope along the route, and the curvature of curves along the route.

[0061] The characteristic information of the area around the route includes information on popular stores around the route, information on schools around the route, the average age of drivers living around the route, the number of commuters living around the route, and information on whether the area around the route is a business district or a commuter town. The characteristic information of the destination includes information on the number of delivery points and the number of bus stops on the route in the case of a parcel delivery service.

[0062] As illustrated in Fig. 12, the evaluation pattern v_test may be stored in correspondence with the average vehicle speed v_ave and the number of traffic lights. If the average vehicle speed v_ave is "v0" and the number of traffic lights is "0 to 10", the evaluation pattern v_test is selected as "S0".

[0063] [Note] Notes 1 to 3 below can be combined in any way as long as there is no technical contradiction.

[0064] [Appendix 1] a pattern storage unit 204 for storing a plurality of vehicle speed patterns as evaluation patterns in association with evaluation traffic information, which is information relating to traffic when a vehicle is traveling; A route acquisition unit 102 that acquires a travel route along which a target vehicle, the target of which is to predict energy consumption, travels; an information acquisition unit 103 that acquires target traffic information, which is information regarding traffic when the target vehicle travels along a travel route; a pattern acquisition unit 104 for acquiring an evaluation pattern corresponding to target traffic information from a plurality of evaluation patterns stored in a pattern storage unit 204; The energy prediction device 10 includes an energy prediction unit 105 that predicts energy consumption when a target vehicle travels along a travel route using the evaluation pattern acquired by the pattern acquisition unit 104.

[0065] According to Supplementary Note 1, since an evaluation pattern corresponding to target traffic information, which is information on traffic when a target vehicle travels along a travel route, is acquired, a vehicle speed pattern according to the traffic conditions along the travel route can be selected, and the prediction accuracy of energy consumption can be improved. Since a vehicle speed pattern along the travel route is created using a pre-stored evaluation pattern and energy consumption is predicted, calculations can be performed efficiently while improving prediction accuracy of energy consumption, and the calculation speed can be increased.

[0066] In the description of this embodiment, an example has been described in which, when creating a vehicle speed pattern on a travel route using an evaluation pattern, the evaluation pattern is repeated until the entire travel route is covered. In addition, since the horizontal axis of the evaluation pattern is the time axis, an example has been described in which the horizontal axis is converted to the distance axis to create data that matches the distance of the travel route. The method of creating a vehicle speed pattern on a travel route using an evaluation pattern is not limited to this. For example, if the evaluation pattern has a sufficiently long time, the evaluation pattern may not be repeated, but a part of the evaluation pattern may be used to create the vehicle speed pattern on the travel route. For example, an evaluation pattern with a horizontal axis that is the distance axis may be prepared in advance.

[0067] [Appendix 2] The route acquisition unit 102 divides the travel route into a plurality of sections, The information acquisition unit 103 acquires target traffic information for each section, The pattern acquisition unit 104 acquires an evaluation pattern corresponding to the target traffic information for each section, The energy prediction device 10 according to claim 1, wherein the energy prediction unit 105 predicts energy consumption when the target vehicle travels along the travel route by using the evaluation pattern for each section.

[0068] According to Supplementary Note 2, the driving route is divided into a plurality of sections, target traffic information is acquired for each section, an evaluation pattern for each section is selected, and the evaluation pattern for each section is used to predict the energy consumption, so that the prediction accuracy of the energy consumption can be improved. Also in Supplementary Note 2, a vehicle speed pattern on the driving route is created using a pre-stored evaluation pattern to predict the energy consumption, so that the calculation can be performed efficiently while improving the prediction accuracy of the energy consumption, and the calculation speed can be increased.

[0069] [Appendix 3] The energy prediction device 10 described in Appendix 1 or 2, wherein the evaluation traffic information and the target traffic information include at least one of vehicle speed characteristic information, traffic flow characteristic information, route characteristic information, characteristic information of the surrounding area of ​​the route, destination characteristic information, and weather characteristic information.

[0070] The present embodiment has been described above with reference to specific examples. However, the present disclosure is not limited to these specific examples. Any design modifications made by a person skilled in the art to these specific examples are also included within the scope of the present disclosure as long as they have the features of the present disclosure. The elements of each of the above-mentioned specific examples and their arrangements, conditions, shapes, etc. are not limited to those exemplified and can be changed as appropriate. The combination of each of the elements of each of the above-mentioned specific examples can be changed as appropriate as long as no technical contradiction occurs. [Explanation of symbols]

[0071] 10: Energy forecasting device 101: Vehicle selection section 102: Route acquisition unit 103: Information acquisition department 104: Pattern acquisition section 105: Energy Prediction Department 201: Vehicle characteristics storage section 202: Route information storage section 203: Traffic information storage section 204: Pattern storage section

Claims

1. a pattern storage unit (204) for storing a plurality of vehicle speed patterns as evaluation patterns in association with evaluation traffic information, which is information relating to traffic when a vehicle is traveling; A route acquisition unit (102) for acquiring a travel route along which a target vehicle, the target of which is to predict energy consumption, travels; an information acquisition unit (103) that acquires target traffic information, which is information regarding traffic when the target vehicle travels along the travel route; a pattern acquisition unit (104) for acquiring an evaluation pattern corresponding to the target traffic information from a plurality of the evaluation patterns stored in the pattern storage unit; and an energy prediction unit (105) that predicts energy consumption when the target vehicle travels along the travel route using the evaluation pattern acquired by the pattern acquisition unit.

2. The route acquisition unit divides the travel route into a plurality of sections, The information acquisition unit acquires the target traffic information for each section, the pattern acquisition unit acquires the evaluation pattern corresponding to the target traffic information for each of the sections; The energy prediction device according to claim 1 , wherein the energy prediction unit predicts energy consumption when the target vehicle travels along the travel route by using the evaluation pattern for each section.

3. 3. The energy prediction device according to claim 1, wherein the evaluation traffic information and the target traffic information include at least one of characteristic information of a vehicle speed, characteristic information of a traffic flow, characteristic information of a route, characteristic information of a surrounding area of ​​the route, characteristic information of a destination, and characteristic information of weather.

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