Energy Estimation Device

Abstract

To provide an energy estimation device capable of estimating a cruisable distance in which a vehicle speed variation pattern is considered.SOLUTION: There is provided an energy estimation device (2) comprising: a probability setting part (32) for setting a vehicle speed probability being a probability at which a vehicle on a travel route identified by travel route information travels at a specific vehicle speed: a vehicle speed pattern estimation part (52) for estimating a vehicle speed variation pattern of the vehicle on the travel route on the basis of the travel route information and the vehicle speed probability; a travel load estimation part (54) for estimating a travel load property of the vehicle on the travel route; and an energy estimation part (55) for estimating energy required for travel of the vehicle using the travel load property and the vehicle speed variation pattern.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an energy estimation apparatus. [Background technology]

[0002] Whether the vehicle is an electric vehicle that uses a battery as its energy source or an internal combustion engine vehicle that uses fossil fuels as its energy source, it is necessary to know how much range remains in order to operate the vehicle stably.

[0003] In Patent Document 1 below, one method for estimating a cruising range is to estimate a driving pattern from route information and traffic information, and estimate a driving load based on gradients and acceleration, and then estimate the cruising range. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-108014 Summary of the Invention [Problem to be solved by the invention]

[0005] In Patent Document 1, only the gradient of the road on which the vehicle is traveling and the acceleration of the vehicle are taken into consideration. Deceleration and stopping, which are important for estimating the power consumption and fuel consumption during deceleration, occur at predetermined locations such as intersections with traffic lights and bus stops. However, the occurrence of these events is not certain.

[0006] An object of the present disclosure is to provide an energy estimation device capable of estimating a cruising range taking into account a vehicle speed fluctuation pattern. [Means for solving the problem]

[0007] The energy estimation device (2) according to the present disclosure includes a probability setting unit (32), a vehicle speed pattern estimation unit (52), a road load estimation unit (54), and an energy estimation unit (55). The probability setting unit sets a vehicle speed probability, which is the probability that a vehicle will reach a specific vehicle speed on a road route specified by road route information. The vehicle speed pattern estimation unit estimates a vehicle speed fluctuation pattern of the vehicle on the road route based on the road route information and the vehicle speed probability. The road load estimation unit estimates road load characteristics of the vehicle on the road route. The energy estimation unit estimates energy required for vehicle travel using the road load characteristics and the vehicle speed fluctuation pattern. The vehicle speed pattern estimation unit uses the vehicle speed probability to estimate a vehicle speed fluctuation pattern when the vehicle stops at a specific point as a stopping pattern, and uses the vehicle speed probability to estimate a vehicle speed fluctuation pattern when the vehicle passes through the specific point as a passing pattern.The running load estimation unit estimates running load characteristics of the vehicle corresponding to each of the stopping pattern and the passing pattern.The energy estimation unit estimates energy required for running the vehicle corresponding to each of the stopping pattern and the passing pattern. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to provide an energy estimation device capable of estimating a cruising range taking into account a vehicle speed fluctuation pattern. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating the energy estimation device according to this embodiment. [Figure 2] FIG. 2 is a flowchart illustrating an information processing flow using the energy estimation device shown in FIG. [Figure 3] FIG. 3 is a diagram illustrating an example of acceleration information. [Figure 4] FIG. 4 is a diagram illustrating an example of deceleration information. [Figure 5] FIG. 5 is a diagram illustrating an example of the driving data. [Figure 6] FIG. 6 is a diagram illustrating an example of stop probability data. [Figure 7] FIG. 7 is a diagram showing an example of vehicle speed estimation data. [Figure 8] FIG. 8 is a diagram showing an example of converting the relationship between vehicle speed and distance into the relationship between vehicle speed and time. [Figure 9] FIG. 9 is a diagram illustrating an example of energy estimation. [Figure 10]FIG. 10 is a diagram for explaining energy estimation in an electric vehicle. [Figure 11] FIG. 11 is a diagram illustrating an example of the efficiency of the electrical system in the energy estimation. [Figure 12] FIG. 12 is a diagram showing an example of engine efficiency in energy estimation in an engine vehicle. [Figure 13] FIG. 13 is a diagram showing an example of engine efficiency in energy estimation. [Figure 14] FIG. 14 is a diagram showing an example of information display. [Figure 15] FIG. 15 is a flowchart illustrating an information processing flow using the energy estimation device shown in FIG. 1, which includes a process for calculating a vehicle speed fluctuation pattern from accumulated vehicle speed data. [Figure 16] FIG. 16 is a diagram showing an example of calculating acceleration, deceleration, and vehicle speed. [Figure 17] FIG. 17 is a diagram illustrating an example of calculating the stop probability. [Figure 18] FIG. 18 is a flowchart illustrating an information processing flow using the energy estimation device shown in FIG. 1, which includes a process for performing energy estimation for each driver. [Figure 19] FIG. 19 is a flowchart illustrating an information processing flow using the energy estimation device shown in FIG. 1, which includes a path probability calculation process that takes time into consideration. [Figure 20] FIG. 20 is a flowchart illustrating an information processing flow using the energy estimation device shown in FIG. 1, which includes a route probability calculation process that takes weather conditions into consideration. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the present embodiment will be described with reference to the accompanying drawings. To facilitate understanding of the description, the same components in the drawings will be denoted by the same reference numerals as much as possible, and duplicated descriptions will be omitted.

[0011] 1 is a diagram illustrating functional components of an energy estimation device 2 according to this embodiment. As shown in FIG. 1, the energy estimation device 2 includes a travel data storage unit 20, a characteristic setting unit 31, a probability setting unit 32, a vehicle speed characteristic information storage unit 41, a travel route information storage unit 42, a route traffic information storage unit 43, a vehicle speed probability information storage unit 44, a travel load information storage unit 45, a driver selection unit 51, a vehicle speed pattern estimation unit 52, a travel load estimation unit 54, an energy estimation unit 55, an efficiency calculation unit 56, and an information display unit 57. The energy estimation device 2 is a computer system having a hardware configuration including a CPU, a memory, a communication interface, and the like.

[0012] Vehicle 10 is an example of a vehicle that is the target of estimation by the energy estimation device 2 of this embodiment. Vehicle 10 is connected to a network NW. Traveling data of vehicle 10 is transmitted to traveling data storage unit 20 via the network NW. For convenience of illustration, only one vehicle 10 is shown, but there may be many vehicles from which traveling data is collected and many vehicles from which energy is estimated.

[0013] The driving data storage unit 20 is a part that stores driving data transmitted from the vehicle 10. The driving data includes information related to driving, such as information about the acceleration and deceleration of the vehicle 10 and information about driving time. The driving data may be information about the acceleration and deceleration of the vehicle 10 and information about driving time for each driver. The driving data may be information about the acceleration and deceleration of the vehicle 10 and information about driving time, correlated with time information when a driving state of the vehicle 10 occurred. The driving data may be information about the acceleration and deceleration of the vehicle 10 and information about driving time, correlated with weather information when a driving state of the vehicle 10 occurred.

[0014] The characteristic setting unit 31 is a part that sets the basic vehicle speed characteristics of the vehicle 10 based on the driving data stored in the driving data storage unit 20. The basic vehicle speed characteristics include information on acceleration and deceleration of the vehicle speed fluctuation pattern. The basic vehicle speed characteristics also include traffic information of the route. The characteristic setting unit 31 stores the information on acceleration and deceleration in the vehicle speed characteristic information storage unit 41. The characteristic setting unit 31 stores the traffic information of the route in the route traffic information storage unit 43.

[0015] The probability setting unit 32 is a part that sets the vehicle speed probability, which is the probability that a vehicle will reach a specific vehicle speed on a travel route. The vehicle speed for which the vehicle speed probability is set can be arbitrarily determined. As an example, the vehicle speed probability can be set to the stopping probability at which the vehicle speed becomes zero and the vehicle stops. At a point where the stopping probability is extremely low and vehicles essentially do not stop, the stopping probability is set to 0%. At a point where the stopping probability is extremely high and vehicles essentially all stop, the stopping probability is set to 100%. For example, at a point on a highway where traffic congestion almost never occurs, the stopping probability is set to 0%. For example, at a stop sign, vehicles will stop as long as traffic laws are observed, so the stopping probability is set to 100%. If the point is a bus stop, for example, the stopping probability is set to 80% for a bus stop with many passengers and 20% for a bus stop with few passengers.

[0016] The vehicle speed characteristic information storage unit 41 is a unit that stores vehicle speed fluctuation patterns as vehicle speed characteristic information. The vehicle speed characteristic information may be set for each vehicle model, or may be set as information common to multiple vehicle models. For example, as shown in FIG. 3, information specifying the relationship between vehicle speed and acceleration is stored as a vehicle speed fluctuation pattern. Also, as shown in FIG. 4, information specifying the relationship between vehicle speed and deceleration is stored as a vehicle speed fluctuation pattern. The vehicle speed fluctuation patterns stored in the vehicle speed characteristic information storage unit 41 are not limited to these. The acceleration or deceleration may be a fixed value. The vehicle speed characteristic information may be stored in advance, obtained from a server each time, or set by the user.

[0017] The driving route information storage unit 42 is a part that stores route information of the driving route that the vehicle 10 will travel until the timing of executing control to estimate the cruising distance. Here, the route information is, for example, information including latitude information, longitude information, altitude information, and point type information along the driving route that the vehicle 10 is scheduled to travel from the current location to the destination. The latitude information is information that indicates the latitude of a certain point. The longitude information is information that indicates the longitude of a certain point. The altitude information is information that indicates the altitude of a certain point.

[0018] The point type information is information that indicates the type of a certain point. The point type specifies factors that affect changes in the vehicle's speed. For example, type type "0" indicates "driving through," type type "1" indicates "traffic light, stopping point," type type "2" indicates "bus stop, stopping point," type type "3" indicates "temporary stop," and type type "4" indicates "turning point, no traffic light."

[0019] The route traffic information storage unit 43 is a part that stores traffic information about the driving route that the vehicle 10 will travel up to the time when the cruising distance is estimated. Here, the traffic information refers to, for example, information about congestion on the driving route, construction information, accident information, and driving conditions that affect the vehicle's driving speed, such as the presence or absence of intersections and traffic lights. The traffic information also includes information about the legal speed limit.

[0020] The vehicle speed probability information storage unit 44 is a part that stores the vehicle speed probability of the driving route that the vehicle 10 will travel until the timing of estimating the cruising distance. The stopping probability calculated by the probability setting unit 32 is stored in the vehicle speed probability information storage unit 44 as the vehicle speed probability.

[0021] The road load information storage unit 45 is a part that stores road load information required for estimating the road load. The road load is calculated using acceleration resistance, air resistance, gradient resistance, and rolling resistance, and therefore, this information is stored as road load information.

[0022] The vehicle speed characteristic information storage unit 41, the driving route information storage unit 42, the route traffic information storage unit 43, the vehicle speed probability information storage unit 44, and the driving load information storage unit 45 may be provided in physically different memory devices, or may be integrated into a single memory device, for example.

[0023] The driver selection unit 51 is a part that selects a driver to be estimated. The driver selection unit 51 outputs information that identifies the selected target driver to the vehicle speed pattern estimation unit 52.

[0024] The vehicle speed pattern estimation unit 52 is a unit that estimates a pattern of the relationship between distance and vehicle speed based on the probability of the vehicle speed and the acceleration / deceleration pattern. When a specific target driver is selected, the vehicle speed pattern estimation unit 52 estimates a pattern of the relationship between vehicle speed distance and vehicle speed that suits the target driver. The vehicle speed pattern estimation unit 52 estimates a pattern of the relationship between vehicle speed and time by taking into account the time for which the vehicle speed is maintained in addition to the pattern of the relationship between distance and vehicle speed.

[0025] The running load estimation unit 54 is a part that estimates the running load based on the pattern of the relationship between distance and vehicle speed estimated by the vehicle speed pattern estimation unit 52 and the running load information stored in the running load information storage unit 45. The energy estimation unit 55 is a part that estimates the energy required for running based on the running load estimated by the running load estimation unit 54.

[0026] The efficiency calculation unit 56 is a unit that calculates efficiency such as electricity cost and fuel efficiency based on the required energy estimated by the energy estimation unit 55 and the traveled distance. The traveled distance may be calculated by integrating the vehicle speed variation pattern estimated by the vehicle speed pattern estimation unit 52 over time, or by integrating the distance between points specified by the latitude information and longitude information. The information display unit 57 is a unit that notifies the user of the efficiency calculated by the efficiency calculation unit 56.

[0027] Next, an information processing flow using the energy estimation device 2 will be described with reference to Fig. 2. In step S101, the vehicle speed pattern estimation unit 52 selects a vehicle model. The vehicle model may be selected based on information input by a user or may be set in advance.

[0028] In step S102 following step S101, vehicle speed pattern estimation unit 52 sets basic characteristics for the vehicle speed fluctuation pattern. More specifically, vehicle speed pattern estimation unit 52 acquires and sets information about acceleration and deceleration of the vehicle speed fluctuation pattern. Vehicle speed pattern estimation unit 52 acquires information about acceleration and deceleration from information stored in vehicle speed characteristic information storage unit 41.

[0029] The vehicle speed pattern estimation unit 52 may acquire information about acceleration and deceleration from information stored in another server. The vehicle speed pattern estimation unit 52 may acquire information about acceleration and deceleration from information manually set by the user. The acceleration and deceleration may be fixed values, or may be defined by a function correlated with the vehicle speed as exemplified in Figures 3 and 4. The vehicle speed pattern estimation unit 52 outputs the basic characteristics of the set vehicle speed variation pattern to the running load estimation unit 54.

[0030] In step S103 following step S102, the vehicle speed pattern estimation unit 52 acquires driving route information. The driving route information is stored in the driving route information storage unit 42. In step S104 following step S103, the vehicle speed pattern estimation unit 52 acquires traffic information. The traffic information is stored in the route traffic information storage unit 43. In step S105 following step S104, the vehicle speed pattern estimation unit 52 acquires a stopping probability. The stopping probability is stored in the vehicle speed probability information storage unit 44.

[0031] 5 and 6, an example of the driving route information, traffic information, and stop probability acquired by the vehicle speed pattern estimation unit 52 will be described. Fig. 5 shows an example of the driving route information. Latitude information and longitude information are set for nine points i=1, 2, 3, 4, 5, 6, 7, 8, and 9.

[0032] FIG. 6 is an example of the driving route information, traffic information, and probability information for each point illustrated in FIG. 5. Point i=1 has latitude (1), longitude (1), altitude (1), type 2, legal speed limit of 50 km / h, and a 100% probability of stopping. Type 2 is a "bus stop, stopping point," and in this example, is assumed to be the starting and ending point of a bus. Point i=2 has latitude (2), longitude (2), altitude (2), type 2, legal speed limit of 50 km / h, and a 10% probability of stopping. Type 2 is a "bus stop, stopping point." Point i=3 has latitude (3), longitude (3), altitude (3), type 1, legal speed limit of 50 km / h, and a 10% probability of stopping. Type 1 is a "traffic light, stopping point."

[0033] Point i=4 has latitude (4), longitude (4), altitude (4), type 3, legal speed limit 50km / h, and a stopping probability of 100%. Type 3 is a "stop location." Point i=5 has latitude (5), longitude (5), altitude (5), type 0, legal speed limit 40km / h, and a stopping probability of 0%. Type 0 is a "passing through." Point i=6 has latitude (6), longitude (6), altitude (6), type 1, legal speed limit 40km / h, and a stopping probability of 80%. Type 1 is a "traffic light, stopping point."

[0034] Point i=7 has latitude (7), longitude (7), altitude (7), type 4, legal speed limit 50km / h, and a 100% probability of stopping. Type 4 is "turn right or left, no traffic light." Point i=8 has latitude (8), longitude (8), altitude (8), type 2, legal speed limit 50km / h, and a 20% probability of stopping. Type 2 is "bus stop, stopping point." Point i=9 has latitude (9), longitude (9), altitude (9), type 2, legal speed limit 50km / h, and a 100% probability of stopping. Type 2 is "bus stop, stopping point."

[0035] The description will continue with reference to Figure 2. In step S106 following step S105, the vehicle speed pattern estimation unit 52 estimates a vehicle speed fluctuation pattern. The vehicle speed pattern estimation unit 52 estimates the vehicle speed fluctuation pattern based on the basic characteristics set in step S102 and the driving route information, traffic information, and stop probability acquired in steps S103, S104, and S105.

[0036] The vehicle speed fluctuation pattern estimated by the vehicle speed pattern estimation unit 52 will be described with reference to Fig. 7. The vehicle speed fluctuation pattern shown in Fig. 7 is estimated by the vehicle speed pattern estimation unit 52 based on the travel route information, traffic information, and stopping probability exemplified in Figs. 5 and 6. The vehicle departing from point i=1 accelerates to the legal speed of 50 km / h. The acceleration is the acceleration set in step S102. Point i=2 has a low stopping probability of 10%, so in this embodiment, it is treated as if the vehicle will pass through without stopping.

[0037] At point i=3, the probability of stopping is low at 10%, so in this embodiment, the vehicle is assumed to pass through without stopping. At point i=4, the probability of stopping is 100%, so the vehicle stops. Vehicles heading towards point i=4 decelerate from the legal speed of 50 km / h. The deceleration is the deceleration set in step S102. Once the vehicle has stopped at point i=4, it accelerates to the legal speed of 40 km / h. The acceleration is the acceleration set in step S102.

[0038] At point i=5, the probability of stopping is 0%, so the vehicle passes through. At point i=6, the probability of stopping is high at 80%, so in this embodiment, the vehicle is treated as stopping there. Vehicles heading towards point i=6 decelerate from the legal speed of 40 km / h. The deceleration is the deceleration set in step S102. Vehicles that have stopped at point i=6 accelerate to the legal speed of 50 km / h. The acceleration is the acceleration set in step S102.

[0039] At point i=7, the probability of stopping is 100%, so the vehicle stops. Vehicles heading towards point i=7 decelerate from the legal speed of 50 km / h. The deceleration is the deceleration set in step S102. Once the vehicle has stopped at point i=7, it accelerates up to the legal speed of 50 km / h. The acceleration is the acceleration set in step S102.

[0040] At point i=8, the probability of stopping is low at 20%, so in this embodiment, the vehicle is assumed to pass through without stopping. At point i=9, the probability of stopping is 100%, so the vehicle will stop. Vehicles heading toward point i=9 will decelerate from the legal speed of 50 km / h. The deceleration will be the deceleration set in step S102.

[0041] After estimating the vehicle speed fluctuation pattern for the position as shown in FIG. 7, the vehicle speed pattern estimation unit 52 converts it into a vehicle speed fluctuation pattern for the time axis. The vehicle speed pattern estimation unit 52 changes the stop time depending on the type of the point. An example of changing the stop time will be described with reference to FIG. 8. FIG. 8(A) illustrates a vehicle speed fluctuation pattern for the position. In the example shown in FIG. 8(A), the first stop point is type 1, and the next stop point is type 2. Since type 1 is "signal, stop point," the stop time is set to 40 seconds. Since type 2 is "bus stop, stop point," the stop time is set to 30 seconds. By reflecting this stop time, it is possible to generate a vehicle speed fluctuation pattern for the time axis shown in FIG. 8(B).

[0042] The explanation will continue with reference to FIG. 2. In step S107 following step S106, the running load estimation unit 54 estimates the running load. The running load can be estimated from acceleration resistance, air resistance, gradient resistance, and rolling resistance. The running load can be indicated by running resistance and running horsepower. The running resistance can be calculated using parameters such as total vehicle weight, air resistance coefficient, frontal projection area, and rolling resistance coefficient. These parameters are stored in the running load information storage unit 45 for each vehicle type selected in step S101.

[0043] Running resistance F drv(t) is estimated 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 the point at time t and the point at time t-1

[0044] The air density ρ is a fixed value of 1.293 kg / m 3 The air density ρ may be calculated from the 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 information as shown in FIG.

[0045] Running horsepower P drv (t) is estimated using the following equation (f02): P drv (t)=F drv (t)*v(t) (f02)

[0046] If the probability of stopping at time t is Prob(t), the running resistance F is calculated using the following equations (f03), (f04), (f05), and (f06) for the vehicle speed fluctuation pattern v2(t) of stopping that can occur with probability Prob(t) and the vehicle speed fluctuation pattern v1(t) of passing that can occur with probability 100-Prob(t). drv_1 (t), F drv_2 (t) and running horsepower P drv_1 (t), P drv_2 (t) can be calculated. The subscripts below are "2" for the stop pattern and "1" for the pass pattern. F drv_1(t)=Wa(t)+0.5*ρ*Cd*Av1 2 (t)+μWg+Wgsinθ1(t) ···(f03) F drv_2 (t)=Wa(t)+0.5*ρ*Cd*Av2 2 (t)+μWg+Wgsinθ2(t) ···(f04) P drv_1 (t)=F drv_1 (t)*v1(t) (f05) P drv_2 (t)=F drv_2 (t)*v2(t) (f06) The calculation results are shown in FIG. 9 as an example.

[0047] The description will continue with reference to Fig. 2. In step S108 following step S107, the energy estimation unit 55 calculates the drive system efficiency. In step S109 following step S108, the energy estimation unit 55 estimates the amount of required energy.

[0048] Figure 10 shows an example of a system for an electric vehicle. In the system shown in Figure 10, the system efficiency of the electrical 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%. 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 equations (f07) and (f08). P´ drv_1 (t)=P drv_1 (t) / R mech ···(f07) P´ drv_2 (t)=P drv_2 (t) / R mech ···(f08)

[0049] Electrical system efficiency R elec is a function of the energy input from the electrical system to the mechanical system, and is determined as shown in Figure 11. elec is the energy P' input to the mechanical system drv It is a function of (t) and can be calculated using the following equations (f09) and (f10). R elec_1 =f(P´ drv_1 (t)) ···(f09) R elec_2 =f(P´ drv_2 (t)) ···(f10)

[0050] Energy P´ supplied to the electrical system for driving drv (t) can be calculated using the following equations (f11) and (f12). P´´ drv_1 (t)=P´ drv_1 (t) / R elec_1 (f11) P´´ drv_2 (t)=P´ drv_2 (t) / R elec_2 (f12)

[0051] The energy required to drive the air conditioner and auxiliary equipment is P other Let (t). P other (t) may be a fixed value such as 5 kW.

[0052] Using the stoppage probability Prob(t), the expected value of the required power P sum (t) is calculated using the following equation (f13). P sum (t)=((100―Prob(t))100*P´´ drv_1 (t)+Prob(t) / 100*P´´ drv_2 (t))+P other (t) (f13)

[0053] P sum When (t)<0, the energy is stored in the battery as regenerative energy. sum (t) is integrated over time and the required energy amount Esum is calculated using the following equation (f14). E sum =Σ(P sum (t)*(t―(t-1))) (f14)

[0054] This embodiment can be applied not only to electric vehicles but also to engine vehicles. Fig. 12 shows an example of a system for an engine vehicle. In the system shown in Fig. 12, 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%. 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 equations (f15) and (f16). P´ drv_1 (t)=P drv_1 (t) / R mech (f15) P´ drv_2 (t)=P drv_2 (t) / R mech (f16)

[0055] In addition to the energy required for driving, the engine also supplies energy to drive the air conditioner and auxiliary equipment. other Let (t). P other (t) may be a fixed value such as 5 kW.

[0056] Engine efficiency R eng is a function of the energy input from the engine to the mechanical system and the energy for driving the accessories, and is determined, for example, as shown in FIG. 13. eng is P sum It is a function of (t) and can be calculated using the following equation (f17). R eng =g(Psum (t)) ···(f17)

[0057] Using the stoppage probability Prob(t), the expected value of the power P' sum (t) is calculated using the following equations (f18) and (f19). P sum (t)=((100―Prob(t))100*P´ drv_1 (t)+Prob(t) / 100*P´ drv_2 (t))+P other (t) (f18) P´ sum (t)=P sum (t) / R eng ···(f19)

[0058] Since engine vehicles do not store regenerative energy, only positive values ​​are considered. P´´ sum (t)=P´ sum (t)(P´ sum (t)>0) (f20)

[0059] The energy estimation unit 55 calculates P' sum (t) is integrated over time and the required energy amount E sum is calculated using the following equation (f21). E sum =Σ(P´´ sum (t)*(t―(t-1))) (f21)

[0060] In step S110 following step S109, the efficiency calculation unit 56 estimates the energy efficiency information. sum The efficiency calculation unit 56 can calculate the travel distance L by integrating the vehicle speed fluctuation pattern over time. The efficiency calculation unit 56 may also calculate the travel distance from latitude information and longitude information. The energy efficiency EC in the case of an electric vehicle ev can be calculated using the following equation (f22): EC ev =L / E sum (f22)

[0061] The efficiency calculation unit 56 calculates the energy efficiency EC ev and battery capacity SOC, the cruising range L drv_ev is calculated using the following equation (f23). L drv_ev =SOC*EC ev (f23)

[0062] In the case of an engine vehicle, the calorific value is C fuel Using this, the amount of energy E sum Fuel amount L fuel The efficiency calculation unit 56 calculates the fuel amount L using the following formula (f24): fuel Calculate. L fuel =E sum / C fuel ···(f24)

[0063] The efficiency calculation unit 56 calculates the energy efficiency EC icev Calculate. EC icev =L / L fuel ···(f25)

[0064] The efficiency calculation unit 56 calculates the energy efficiency EC icev and fuel load L fuel_tank Using this, the cruising range L drv_icev is calculated using the following equation (f26). L drv_icev =L drv_icev *EC icev ···(f26)

[0065] In step S111 following step S110, the information display unit 57 notifies the user of information related to the energy efficiency calculated in step S110. The manner of notification is not particularly limited, and the user may be notified of information such as that shown in FIG. 14 using an app or the like.

[0066] Next, the manner in which the basic characteristics of a vehicle speed fluctuation pattern are set will be described with reference to Fig. 15. In step S201, the characteristics setting unit 31 calculates the basic characteristics of a vehicle speed fluctuation pattern using data stored in the traveling data storage unit 20. An example of the information stored in the traveling data storage unit 20 is shown in Fig. 16.

[0067] 16(A) shows a situation where data on acceleration situations in which the vehicle speed increases over time is accumulated. The average acceleration can be calculated based on the data shown in FIG. 16(A).

[0068] 16(B) shows a situation where data on deceleration situations in which the vehicle speed decreases over time is accumulated. The average deceleration can be calculated based on the data shown in FIG. 16(B).

[0069] Figure 16(C) shows a situation where data is accumulated when the vehicle speed is within a certain range over time. The average steady-state speed can be calculated based on the data shown in Figure 16(C). For example, if many vehicles are traveling at speeds higher than the legal speed limit, a vehicle speed can be set that is more in line with the actual situation.

[0070] In step S202 following step S201, the probability setting unit 32 calculates stop probability information for the route. For example, if the location type is "0", the probability setting unit 32 does not calculate the stop probability because it is "passing by". For example, if the location type is "1", the probability setting unit 32 calculates the stop probability because it is "signal, stop point". For example, if the location type is "2", the probability setting unit 32 calculates the stop probability because it is "bus stop, stop point".

[0071] For example, the probability setting unit 32 sets a certain area including the point where the stopping probability is calculated as the calculation target area, and treats a stop within the calculation target area as a stop. As shown in the example of Figure 17, there are two stops within the calculation target area, but in this case, it may be treated as one stop.

[0072] If there are M occurrences of stopping data among the N pieces of data in the calculation target area, the probability setting unit 32 calculates the stopping probability Prob(i) using the following equation (f27). Prob(i)=M / N*100 (f27)

[0073] When the process of step S202 is completed, the process proceeds to step S101 in Fig. 2. The subsequent processes are the same as those already explained, so the explanation thereof will be omitted.

[0074] Next, a mode for setting the basic characteristics of a vehicle speed fluctuation pattern for each driver will be described with reference to Fig. 18. In step S301, the driver selection unit 51 sets a target driver. The driver selection unit 51 sets the target driver in accordance with a selection operation on the screen by the user, etc.

[0075] In step S302 following step S301, the vehicle speed pattern estimation unit 52 extracts the acceleration data, deceleration data, and average steady vehicle speed data of the selected driver as the basic characteristics of the vehicle speed fluctuation pattern.

[0076] When the process of step S302 is completed, the process proceeds to step S101 in Fig. 2. The subsequent processes are the same as those already explained, so the explanation thereof will be omitted.

[0077] Next, with reference to Fig. 19, a manner in which route probability information is calculated for each time will be described. In step S401, the probability setting unit 32 sets a target time. In step S402 following step S401, route probability information for the set target time is calculated. More specifically, the stopping probability of a location at the target time is calculated based on vehicle speed data accumulated in association with the time. The vehicle speed pattern estimation unit 52 uses the data for the target time as vehicle speed probability information.

[0078] When the process of step S402 is completed, the process proceeds to step S101 in Fig. 2. The subsequent processes are the same as those already explained, so the explanation thereof will be omitted.

[0079] Next, with reference to FIG. 20, a mode for calculating route probability information that takes weather into consideration will be described. Travel data is stored in the travel data storage unit 20 in association with weather information. In step S501, the probability setting unit 32 sets a target time. In step S502 following step S501, the weather is set. In step S503 following step S502, stopping probability information corresponding to the time information and weather information is calculated. More specifically, the stopping probability of a location at a target time and weather is calculated based on vehicle speed data accumulated in association with the time and weather. The vehicle speed pattern estimation unit 52 uses this data for the target time and weather as vehicle speed probability information.

[0080] When the process of step S503 is completed, the process proceeds to step S101 in Fig. 2. The subsequent processes are the same as those already explained, so the explanation thereof will be omitted.

[0081] The energy estimation device 2 according to this embodiment includes a probability setting unit 32, a vehicle speed pattern estimation unit 52, a road load estimation unit 54, and an energy estimation unit 55. The probability setting unit 32 sets a vehicle speed probability, which is the probability that a vehicle will reach a specific vehicle speed on a travel route specified by travel route information. The vehicle speed pattern estimation unit 52 estimates a vehicle speed fluctuation pattern of the vehicle on the travel route based on the travel route information and the vehicle speed probability. The travel load estimation unit 54 estimates the travel load characteristics of the vehicle on the travel route. The energy estimation unit 55 estimates the energy required for the vehicle to travel using the road load characteristics and the vehicle speed fluctuation pattern.

[0082] The vehicle speed fluctuation pattern is estimated using the vehicle speed probability, which is the probability that the vehicle will reach a specific speed, and the energy required for driving is estimated together with the driving load, so energy can be estimated more accurately.

[0083] In this embodiment, the vehicle speed probability is the probability that the vehicle will stop at a specific point. Since a stopped state due to a change in vehicle speed has a large impact on the energy required for traveling, using the stopping probability as the vehicle speed probability allows for more realistic energy estimation.

[0084] In this embodiment, the vehicle speed pattern estimation unit 52 uses the vehicle speed probability to estimate a vehicle speed fluctuation pattern when the vehicle stops at a specific point as a stopping pattern, and uses the vehicle speed probability to estimate a vehicle speed fluctuation pattern when the vehicle passes through a specific point as a passing pattern. The running load estimation unit 54 estimates the running load characteristics of the vehicle corresponding to each of the stopping pattern and the passing pattern. The energy estimation unit 55 estimates the energy required for the vehicle to travel corresponding to each of the stopping pattern and the passing pattern.

[0085] By combining the stopping pattern and the passing pattern, it is possible to estimate the energy according to the expected value of each.

[0086] In this embodiment, the vehicle speed probability is the stopping probability that a vehicle will stop at a specific point. The vehicle speed pattern estimation unit 52 estimates a stopping pattern using the stopping probability. The vehicle speed pattern estimation unit 52 calculates a passing probability when a vehicle passes a specific point using the stopping probability, and estimates a passing pattern using this passing probability.

[0087] Since a stopped state during vehicle speed fluctuation has a large impact on the energy required for driving, estimating the stopping pattern using the stopping probability allows for more realistic energy estimation. Since a non-stop state is a passing state, the passing probability can be calculated using the stopping probability. Estimating the passing pattern using the passing probability allows for more realistic energy estimation.

[0088] In this embodiment, the vehicle speed pattern estimation unit 52 estimates the vehicle speed fluctuation pattern using traffic information on the travel route. By using the traffic information, it is possible to estimate energy consumption that is closer to the actual situation.

[0089] In this embodiment, the energy estimation device 2 includes a travel data storage unit 20 that accumulates travel data of ordinary vehicles. A probability setting unit 32 sets a stop probability using the travel data. A vehicle speed pattern estimation unit 52 estimates a vehicle speed fluctuation pattern using the travel data.

[0090] In this embodiment, the energy estimation device 2 includes a driver selection unit 51 that selects a driver. The driving data storage unit 20 stores driving data of general vehicles in association with the driver. The vehicle speed pattern estimation unit 52 estimates a vehicle speed fluctuation pattern using the driving data associated with the driver selected by the driver selection unit 51.

[0091] In this embodiment, the travel data storage unit 20 stores travel data of ordinary vehicles in association with time periods. The probability setting unit 32 sets a stop probability using the travel data for each time period.

[0092] In this embodiment, the travel data storage unit 20 stores travel data of general vehicles in association with weather. The probability setting unit sets the stopping probability using the travel data associated with weather.

[0093] The present embodiment has been described above with reference to specific examples. However, the present disclosure is not limited to these specific examples. Design modifications to these specific examples made by a person skilled in the art as appropriate are also included within the scope of the present disclosure as long as they comprise the features of the present disclosure. The elements of the above-described specific examples, as well as their arrangement, conditions, shape, etc., are not limited to those exemplified and can be modified as appropriate. The elements of the above-described specific examples can be combined in various ways as appropriate, as long as no technical contradictions arise. [Explanation of symbols]

[0094] 2: Energy estimation device 31: Characteristics setting section 32: Probability setting section 51: Driver selection section 52: Vehicle speed pattern estimation unit 54: Road load estimation unit 55: Energy estimation unit 56: Efficiency calculation section

Claims

1. An energy estimation device, comprising: a probability setting unit (32) that sets a vehicle speed probability, which is the probability that a vehicle on a travel route specified by the travel route information will reach a specific vehicle speed; a vehicle speed pattern estimation unit (52) that estimates a vehicle speed fluctuation pattern of the vehicle on the travel route based on the travel route information and the vehicle speed probability; a travel load estimation unit (54) that estimates a travel load characteristic of the vehicle on the travel route; an energy estimation unit (55) that estimates energy required for the vehicle to travel using the traveling load characteristics and the vehicle speed variation pattern, The vehicle speed pattern estimation unit using the vehicle speed probability to estimate the vehicle speed fluctuation pattern when the vehicle stops at a specific point as a stopping pattern; using the vehicle speed probability to estimate the vehicle speed fluctuation pattern when the vehicle passes through a specific point as a passing pattern; the running load estimation unit estimates running load characteristics of the vehicle corresponding to each of the stopping pattern and the passing pattern; The energy estimation unit is an energy estimation device that estimates the energy required for the vehicle to travel corresponding to each of the stopping pattern and the passing pattern.

2. 2. The energy estimation device according to claim 1, An energy estimation device, wherein the vehicle speed probability is a stopping probability that the vehicle will stop at a specific point.

3. 2. The energy estimation device according to claim 1, The vehicle speed probability is a stopping probability that the vehicle will stop at a specific point, The vehicle speed pattern estimation unit estimating the stopping pattern using the stopping probability; An energy estimation device that calculates a passing probability when the vehicle passes through a specific point using the stopping probability, and estimates the passing pattern using this passing probability.

4. The energy estimation device according to any one of claims 1 to 3, The vehicle speed pattern estimation unit estimates the vehicle speed fluctuation pattern using traffic information on the travel route.

5. 4. The energy estimation device according to claim 2 or 3, Further, a travel data storage unit (20) is provided for storing travel data of general vehicles, The probability setting unit sets the stopping probability using the traveling data.

6. The energy estimation device according to any one of claims 1 to 3, Further, a travel data storage unit (20) is provided for storing travel data of general vehicles, The vehicle speed pattern estimation unit estimates the vehicle speed fluctuation pattern using the travel data.

7. The energy estimation device according to any one of claims 1 to 3, Furthermore, a driver selection unit (51) for selecting a driver; a travel data storage unit (20) that stores travel data of a general vehicle in association with a driver; The vehicle speed pattern estimation unit estimates the vehicle speed fluctuation pattern using the driving data associated with the driver selected by the driver selection unit.

8. 4. The energy estimation device according to claim 2 or 3, Furthermore, a travel data storage unit (20) is provided for storing travel data of general vehicles in association with time periods, The probability setting unit sets the stopping probability using the driving data for each time period.

9. 4. The energy estimation device according to claim 2 or 3, Furthermore, a travel data storage unit (20) is provided for storing travel data of general vehicles in association with weather, The probability setting unit sets the stopping probability using the travel data associated with weather.

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