Energy prediction device

Abstract

An energy prediction device that generates vehicle speed prediction information by predicting a vehicle speed change state of a vehicle on an expected travel path on which the vehicle is to travel; using the vehicle speed prediction information to predict the required energy required when the vehicle travels on an expected travel path; and obtaining change information affecting the prediction of the required energy. The energy prediction device predicts the required energy on the basis of the acquired change information.

Description

Technical Field

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

[0002] In JP 2016-049922A, the energy consumption of an electric vehicle is calculated based on a speed pattern and a gradient pattern. A correction parameter is generated based on vehicle information. Then, the energy consumption is corrected using the correction parameter.

[0003] As described in paragraph 0034 of JP 2016-049922A, the correction parameter is estimated by actually measuring and determining the energy consumption under specific conditions at an arbitrary position. Then, the energy consumption is corrected using the estimated correction parameter.

[0004] In JP 2016-049922A, energy consumption reflecting the occurrence of external disturbances can be estimated. However, future changes in parameters that may affect the prediction of required energy required for vehicle travel cannot be considered. Summary of the invention

[0005] Therefore, it is desirable to predict the required energy required for vehicle travel while reflecting future changing factors that affect the required energy.

[0006] One aspect of the present disclosure provides an energy prediction device, which generates vehicle speed prediction information by predicting the vehicle speed change state on an expected driving path to be traveled by the vehicle; uses the vehicle speed prediction information to predict the required energy required for the vehicle to travel on the expected driving path; and predicts the required energy while reflecting the predicted change information that affects the required energy.

[0007] As a result of aspects of the present disclosure, it is possible to predict the required energy required for vehicle travel, and reflect future changing factors that affect the required energy in the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In the attached picture:

[0009] Figure 1 is a block diagram for explaining the energy prediction device according to the present embodiment;

[0010] Figure 2 Is used for use Figure 1 A flowchart illustrating the information processing flow of the energy prediction device shown;

[0011] Figure 3 is a diagram showing an example of change information related to weight;

[0012] Figure 4 is a diagram showing an example of change information related to rolling resistance;

[0013] Figure 5 is a diagram showing an example of change information related to vehicle speed;

[0014] Figure 6 is a diagram showing an example of change information related to acceleration;

[0015] Figure 7 is a diagram for illustrating energy estimation in an electric vehicle;

[0016] Figure 8 is a diagram showing an example of electrical system efficiency in energy estimation;

[0017] Fig. 9 is a diagram showing an example of engine efficiency in energy estimation in an engine car;

[0018] Fig.10 is a diagram showing an example of engine efficiency in energy estimation;

[0019] Fig.11 Is used for use Figure 1 A flowchart illustrating the information processing flow of the energy prediction device shown;

[0020] Fig.12 This is a diagram illustrating an example of the relationship between influencing factors and occurrence probability. DETAILED DESCRIPTION

[0021] Hereinafter, the present embodiment will be described with reference to the accompanying drawings. To facilitate understanding of the description, the same constituent elements are marked with the same reference numerals in the accompanying drawings where possible, and redundant descriptions are omitted.

[0022] Figure 1 2 is a diagram for explaining the functional components of the energy prediction device 2 of this embodiment. Figure 1 As shown, the energy prediction device 2 includes a travel data storage unit 20, a characteristic setting unit 31, a vehicle speed characteristic storage unit 41, a travel route storage unit 42, a route traffic storage unit 43, a change factor storage unit 44, a travel load storage unit 45, a vehicle speed pattern estimation unit 52, a travel load prediction unit 54, an energy prediction unit 55, an efficiency calculation unit 56, and an information display unit 57. In terms of hardware configuration, the energy prediction device 2 is a computer system including a central processing unit (CPU), a memory, a communication interface, and the like.

[0023] The vehicle 10 is an example of a vehicle that is a prediction target of the energy prediction device 2 of the present embodiment. The vehicle 10 is connected to the network NW. The driving data of the vehicle 10 is transmitted to the driving data storage unit 20 via the network NW. For ease of explanation, only a single vehicle 10 is shown. However, driving data can be collected from many vehicles. In addition, the target of energy prediction can be many vehicles.

[0024] The travel data storage unit 20 stores therein travel data transmitted from the vehicle 10. The travel data includes information related to travel, such as information related to the acceleration and deceleration of the vehicle 10 and information related to the travel time. As the travel data, the information related to the acceleration and deceleration of the vehicle 10 and the information related to the travel time may be accumulated for each driver. Furthermore, as the travel data, the information related to the acceleration and deceleration of the vehicle 10 and the information related to the travel time may be accumulated in association with time information related to the time when the travel state of the vehicle 10 occurs. Furthermore, as the travel data, the information related to the acceleration and deceleration of the vehicle 10 and the information related to the travel time may be accumulated in association with weather information related to the weather when the travel state of the vehicle 10 occurs.

[0025] The characteristic setting unit 31 sets the vehicle speed basic characteristic of the vehicle 10 based on the travel data stored in the travel data storage unit 20. The vehicle speed basic characteristic includes information related to acceleration and deceleration in the vehicle speed change pattern. The vehicle speed basic characteristic also includes traffic information related to the path. The characteristic setting unit 31 stores the information related to acceleration and deceleration in the vehicle speed characteristic storage unit 41. The characteristic setting unit 31 stores the traffic information related to the path in the path traffic storage unit 43.

[0026] The vehicle speed characteristic storage unit 41 stores therein a vehicle speed variation pattern as vehicle speed characteristic information. The vehicle speed characteristic information may be set for each vehicle type. Alternatively, the vehicle speed characteristic information may be set to information common to a plurality of vehicle types. For example, information identifying the relationship between vehicle speed and acceleration may be stored as a vehicle speed variation pattern. In addition, for example, information identifying the relationship between vehicle speed and deceleration may be stored as a vehicle speed variation pattern. The vehicle speed variation pattern stored in the vehicle speed characteristic storage unit 41 is not limited thereto. The acceleration or deceleration may be a fixed value. The vehicle speed characteristic information may be stored in advance. Alternatively, the vehicle speed characteristic information may be acquired from a server or set by a user each time.

[0027] The driving path storage unit 42 stores therein path information related to the driving path that the vehicle 10 drives until the time when the control of estimating the cruising range is performed. Here, for example, the path information may include latitude information, longitude information, elevation information, and position classification information along the path from the current position to the destination on the driving path that the vehicle 10 is expected to drive. The latitude information indicates the latitude at a specific position. The longitude information indicates the longitude at a specific position. The elevation information indicates the elevation at a specific position.

[0028] The location classification information indicates the classification of a specific location. The classification of the location identifies factors that affect the change in the vehicle speed of the vehicle. For example, the classification type "0" may indicate "driving past". The classification type "1" may indicate "vehicle stop: traffic light". The classification type "2" may indicate "vehicle stop: bus stop". The classification type "3" may indicate "temporary parking position". The classification type "4" may indicate "left / right turn position, no traffic light". The path traffic storage unit 43 stores therein traffic information related to the driving path on which the vehicle 10 is traveling until the moment when the estimation of the cruising range is performed. Here, for example, the traffic information indicates congestion information, construction information, accident information, and driving conditions that affect the vehicle's driving speed, such as the presence / absence of intersections and traffic lights, related to the driving path. The traffic information also includes information related to the legal speed limit. The change factor storage unit 44 stores therein change information that affects the prediction of the required energy required when the vehicle travels on the expected driving path. The change information includes information that identifies the influencing factors that affect the prediction of the required energy and their probability of occurrence. The change information includes information for identifying the influencing factors affecting the prediction of the required energy and their range of change. The range of change can be identified by the maximum and minimum values ​​of the influencing factors. The range of change can be identified by the maximum and minimum values ​​of the range in which the probability of occurrence of the influencing factors exceeds the threshold. The change information will be described in detail below.

[0029] The travel load storage unit 45 stores therein travel load information required for estimating the travel load. The travel load is calculated using acceleration resistance, air resistance, gradient resistance, and rolling resistance. Therefore, information on acceleration resistance, air resistance, gradient resistance, and rolling resistance is stored as the travel load information.

[0030] The vehicle speed characteristic storage unit 41, the travel route storage unit 42, the route traffic storage unit 43, the change factor storage unit 44, and the travel load storage unit 45 may be provided in physically different storage devices. Alternatively, for example, the vehicle speed characteristic storage unit 41, the travel route storage unit 42, the route traffic storage unit 43, the change factor storage unit 44, and the travel load storage unit 45 may be provided such as integrated in a single storage device.

[0031] The vehicle speed pattern estimation unit 52 estimates a pattern of the relationship between the distance and the vehicle speed based on the vehicle speed probability 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 the distance and the vehicle speed suitable for the target driver. The vehicle speed pattern estimation unit 52 estimates a pattern of the relationship between the vehicle speed and time by considering the amount of time the vehicle speed is maintained in the relationship pattern between the distance and the vehicle speed.

[0032] The travel load prediction unit 54 predicts the travel load based on the relationship pattern between the distance and the vehicle speed estimated by the vehicle speed pattern estimation unit 52 and the travel load information stored in the travel load storage unit 45. The energy prediction unit 55 predicts the required energy required for traveling based on the travel load predicted by the travel load prediction unit 54.

[0033] The efficiency calculation unit 56 calculates efficiency, such as power consumption and fuel consumption, based on the required energy and the travel distance predicted by the energy prediction unit 55. The travel distance may be determined by time integration of the vehicle speed change pattern estimated by the vehicle speed pattern estimation unit 52. Alternatively, the travel distance may be determined by integrating the distance between the positions identified by the latitude information and the longitude information. The information display unit 57 notifies the user of the efficiency calculated by the efficiency calculation unit 56.

[0034] Next, refer to Figure 2 The information processing flow using the energy prediction device 2 is described below. In step S101, the driving load prediction unit 54 sets vehicle information. The vehicle information is information used to calculate the required energy. The vehicle information includes the gross vehicle weight, air resistance coefficient, front projection area, etc. The driving load prediction unit 54 obtains the vehicle information from the information stored in the driving load storage unit 45.

[0035] In step S102 after step S101, the vehicle speed pattern estimation unit 52 sets the vehicle speed change pattern. Various methods can be used to set the vehicle speed change pattern. The vehicle speed pattern estimation unit 52 can set the vehicle speed change pattern by acquiring information related to acceleration and deceleration. The vehicle speed pattern estimation unit 52 acquires information related to acceleration and deceleration from the information stored in the vehicle speed characteristic storage unit 41.

[0036] The vehicle speed pattern estimation unit 52 can also set a vehicle speed change pattern to be newly used by selecting a vehicle speed change pattern from vehicle speed change patterns accumulated in the past. The vehicle speed pattern estimation unit 52 can also set a vehicle speed change pattern to be newly used by combining a plurality of vehicle speed change patterns accumulated in the past. The past vehicle speed change patterns are accumulated in the vehicle speed characteristic storage unit 41.

[0037] In step S103 following step S102 , the vehicle speed pattern estimation unit 52 sets probability data of various characteristics. The probability data of various characteristics are set based on the information stored in the vehicle speed characteristic storage unit 41 and the variation factor storage unit 44 .

[0038] Figure 3 is an example graph of vehicle weight and its probability of occurring at each location. Figure 3 In the example, the weight W(x, i) and the probability P of occurrence at position x W (x, i) are accumulated in association. Variation information associated with the vehicle weight and the probability of its occurrence at each position is stored in the variation factor storage unit 44. For example, the weight and the probability of occurrence at each position can be set by accumulating the number of passengers at each position on a fixed-route bus or the like and setting the weight of each person to 60 kg.

[0039] Figure 4 is an example graph of the rolling resistance coefficient and its probability of occurrence at each location. Figure 4 In the example in , the rolling resistance coefficient u(x, i) and the probability P of occurring at position x are u (x, i) are accumulated in association. Change information associated with the rolling resistance coefficient and its probability of occurrence at each position is stored in the change factor storage unit 44. For example, data of the rolling resistance coefficient u calculated in the past can be accumulated. The rolling resistance coefficient u can be preset based on the road surface state. The road surface state includes road material, road surface shape, snowfall state, icing state, etc.

[0040] Figure 5 is an example graph of the range of vehicle speed variations and their probability of occurrence at each location. Figure 5 In the example, the speed variation range dv(x, i) and the probability P of occurring at position x are dv (x, i) are accumulated in association. The variation information associated with the vehicle speed variation range and the probability of its occurrence at each position is stored in the variation factor storage unit 44. The variation information can be generated based on the data on the vehicle speed pattern stored in the vehicle speed characteristic storage unit 41. The variation information can be stored in the vehicle speed characteristic storage unit 41. Here, the variation information can be the vehicle speed itself, not the vehicle speed variation range.

[0041] Figure 6 is an example graph of the range of acceleration variations and their probability of occurrence at each location. Figure 6 In the example, the acceleration variation range da(x, i) and the probability P of occurring at position x are da(x, i) are accumulated in association. The variation information associated with the acceleration variation range and the probability of its occurrence at each position is stored in the variation factor storage unit 44. The variation information may be generated based on the data on the vehicle speed pattern stored in the vehicle speed characteristic storage unit 41. The variation information may be stored in the vehicle speed characteristic storage unit 41. Here, the variation information may be the acceleration itself, not the acceleration variation range.

[0042] The following will refer to Figure 2 The description is continued. In step S104 following step S103, the travel load prediction unit 54 and the energy prediction unit 55 predict the required energy.

[0043] The travel load prediction unit 54 predicts the travel load based on the vehicle speed change pattern. The travel load is determined as the sum of acceleration resistance, air resistance, gradient resistance, and rolling resistance. The travel load F is predicted using the following expression (f10): drv (t).

[0044] F drv (t)=(ΣP W (x,i)*W(x,i))*(ΣP da (x,i)*(a(t)+da(x,i)))+0.5*ρ*Cd*A(ΣP dv (x,i)*(v(t)+dv(x,i)) 2 )+(ΣP u (x,i)*u(x,i))(ΣP W (x,i)*W(x,i))g+(ΣP W (x,i)*W(x,i))gsinθ(t)…(f01)

[0045] t: time

[0046] a(t): acceleration at time t;

[0047] ρ: air density;

[0048] A: frontal projection area;

[0049] v(t): velocity at time t;

[0050] g: acceleration due to gravity;

[0051] θ(t): The gradient between the position at time t and the position at time t-1.

[0052] The air density r can be 1.293 kg / m 3 The air density ρ can be calculated based on the air temperature. The gravitational acceleration g can be 9.8m / s2 The gradient θ(t) can be determined based on the latitude and longitude information and elevation information in the driving path information, such as Figure 6 as shown in the example.

[0053] The running load prediction unit 54 predicts the running horsepower P using the following expression (f02): drv (t).

[0054] P drv (t) = F drv (t)*ΣP dv (x,i)*(v(t)+dv(x,i))…(f02)

[0055] The energy prediction unit 55 calculates the driving system efficiency. The energy prediction unit 55 estimates the required energy amount.

[0056] Figure 7 An example of a system in the case of an electric vehicle is shown. Figure 7 In the system shown in the example, the system efficiency of the electrical system (MG-INV) is R elec , the efficiency of the mechanical system is R mech A fixed value of 70% can be used as the efficiency R of the mechanical system. mech The efficiency of the mechanical system is R mech The energy input to the mechanical system is expressed in terms of efficiency R mech Transmitted to the drive wheels and generates driving horsepower P drv (t). Therefore, the energy input to the mechanical system is P' drv (t) can be calculated by the following expression (f03).

[0057] P' drv (t) = P drv (t) / R mech …(f03)

[0058] like Fig.11 As shown in the example, the efficiency R of the electrical system elec For example, it is defined by a function of the energy input from the electrical system to the mechanical system.

[0059] Energy P supplied to the electrical system for driving drv (t) can be calculated by the following expression (f04).

[0060] P” drv (t) = P' drv (t) / R elec (P' drv (t))…(f04)

[0061] When the energy represented by expression (f04) is integrated up to time t, the running energy E drv_prd_base It is represented by the following expression (f05).

[0062] E drv_prd_base =Σ(P” drv (t)*(t-(t-1))…(f05)

[0063] When E drv_prd_base When <0, the energy is stored in the battery as regenerative energy.

[0064] The energy required to drive the air conditioner and auxiliary equipment is P other (t).P other (t) can be a fixed value of 5kW. other (t) is integrated until time t, the auxiliary equipment energy E other_prd_base It is represented by the following expression (f06).

[0065] E other_prd_base =Σ(P other (t)*(t-(t-1))…(f06)

[0066] According to expressions (f05) and (f06), the required energy E total_prd_base It is represented by the following expression (f07).

[0067] E total_prd_base (t) = E drv_prd_base (t)+E other_prd_base (t)…(f07)

[0068] In addition to electric vehicles, the present embodiment can also be applied to engine vehicles. Fig. 9 An example of the system in the case of an engine car is shown. Fig. 9 In the example shown in the system, the engine efficiency is R eng The efficiency of a mechanical system is R mech A fixed value of 70% can be used as the efficiency R of the mechanical system. mech The efficiency of the mechanical system is R mech The energy input to the mechanical system is expressed in terms of efficiency R mech Transmitted to the drive wheels and generates driving horsepower P drv (t). Therefore, the energy input to the mechanical system is P' drv (t) can be calculated by the following expression (f08).

[0069] P' drv (t) = P drv (t) / R mech …(f08)

[0070] In addition to the energy used for driving, the energy used to drive the air conditioner and auxiliary equipment is also supplied by the engine. The energy required to drive the air conditioner and auxiliary equipment is P other (t).P other (t) can be a fixed value of 5kW.

[0071] Engine efficiency R eng It is a function of the energy input from the engine to the mechanical system and the energy used to drive auxiliary equipment. For example, the engine efficiency R eng You can Fig.10 Specified as shown in the example.

[0072] The total energy amount P is predicted by the following expressions (f09) and (f10): sum (t).

[0073] P sum (t) = P' drv (t)+P other (t)…(f09)

[0074] P' sum (t) = P sum (t) / R eng (P sum (t))…(f10)

[0075] Since no regenerative energy is stored in an engine vehicle, only positive values ​​are applicable.

[0076] P” sum (t) = P' sum (t)(P' sum (t)>0)···(f11)

[0077] When the energy represented by expression (f11) is integrated up to time t, the required energy amount E total_prd_base It is represented by the following expression (f12).

[0078] E total_prd_base (t)=Σ(P” sum (t)*(t-(t-1))…(f12)

[0079] Here, the driving energy and other energies are predicted in the following manner.

[0080] E drv_prd_base (t) = E total_prd_base (t)*(P' drv (t) / P' sum (t))…(f13)

[0081] Eother_prd_base (t) = E total_prd_base (t)-E drv_prd_base (t)…(f14)

[0082] In step S105 after step S104, the information display unit 57 notifies the user of information related to the energy prediction result. The notification method is not particularly limited. The user may be notified by using an application or the like.

[0083] Next, refer to Fig.11 Another example of the information processing flow using the energy prediction device 2 is described below. In step S201, the travel load prediction unit 54 sets vehicle information. The travel load prediction unit 54 sets vehicle information similarly to the example of referring to Figure 2 Therefore, the description thereof is omitted.

[0084] In step S202 after step S201, the vehicle speed pattern estimation unit 52 sets the vehicle speed change pattern. The vehicle speed pattern estimation unit 54 sets the vehicle speed change pattern similarly to the reference Figure 2 Therefore, the description thereof is omitted.

[0085] In step S203 after step S202, the vehicle speed pattern estimation unit 52 sets the change data of various characteristics. The change data of various characteristics are set based on the information stored in the vehicle speed characteristic storage unit 41 and the change factor storage unit 44. The change data of various characteristics may be the change amount of the numerical value of the parameter used to calculate energy. The change data of various characteristics may be, for example, the maximum value and the minimum value of the parameter used to calculate energy.

[0086] For example, the variation factor storage unit 44 accumulates the maximum value W_max(x) and the minimum value W_min(x) of the weight at the position x. For example, the minimum value and the maximum value of the weight at each position can be set by accumulating the number of passengers at each position on a fixed route bus or the like and setting the weight of each person to 60 kg.

[0087] For example, the variation factor storage unit 44 may accumulate the minimum value u_min(x) and the maximum value u_max(x) of the rolling resistance coefficient at the position x. For example, data of the rolling resistance coefficient u calculated in the past may be accumulated. The rolling resistance coefficient u may be preset based on the road surface conditions. The road surface conditions include road surface material, road surface shape, snowfall conditions, icing conditions, etc.

[0088] For example, the variation factor storage unit 44 may accumulate the minimum value dv_min(x) and the maximum value dv_max(x) of the vehicle speed variation at the position x. The variation information may be generated based on the data on the vehicle speed pattern stored in the vehicle speed characteristic storage unit 41. The minimum value dv_min(x) and the maximum value dv_max(x) of the vehicle speed variation may be stored in the vehicle speed characteristic storage unit 41. Here, the variation information may be the vehicle speed itself, rather than the vehicle speed variation range.

[0089] For example, the variation factor storage unit 44 may accumulate the minimum value da_min(x) and the maximum value da_max(x) of the acceleration variation at the position x. The variation information may be generated based on the data on the vehicle speed pattern stored in the vehicle speed characteristic storage unit 41. The variation information may be stored in the vehicle speed characteristic storage unit 41. Here, the variation information may be the acceleration itself, rather than the acceleration variation range.

[0090] Here, when the occurrence probability is equal to or greater than a predetermined occurrence probability, the minimum and maximum values ​​of the change data such as weight, rolling resistance, vehicle speed, and acceleration may be the minimum and maximum values. Fig.12 As shown in the example in , considering the influencing factor Y, the minimum value Y_min and the maximum value Y_max can be values ​​whose occurrence probability P is equal to or greater than a predetermined value.

[0091] In step S204 following step S203 , the travel load prediction unit 54 and the energy prediction unit 55 predict the required energy.

[0092] The running load prediction unit 54 predicts the running load according to the vehicle speed change pattern. The running load is determined as the sum of acceleration resistance, air resistance, gradient resistance, and rolling resistance. The running load F used as the minimum value is predicted using the following expressions (f21) and (f22): drv_min (t) and the driving load F used as the maximum value drv_max (t).

[0093] F drv_min (t)=W_min(x)*(a(t)+da_min(x))+0.5*ρ*Cd

[0094] *A*(v(t)+dv_min(x)) 2 +u_min(x)*W_min(x)g+W_min(x)*gsinθ(t)…(f21)

[0095] F drv_max (t)=W_max(x)*(a(t)+da_max(x))+0.5*ρ*Cd

[0096] *A*(v(t)+dv_max(x)) 2 +u_max(x)*W_max(x)g+W_max(x)*gsinθ(t)…(f22)

[0097] t: time

[0098] a(t): acceleration at time t;

[0099] ρ: air density;

[0100] A: frontal projection area;

[0101] v(t): velocity at time t;

[0102] g: acceleration due to gravity;

[0103] θ(t): The gradient between the position at time t and the position at time t-1.

[0104] The air density ρ can be 1.293kg / m 3 The air density ρ can be calculated based on the air temperature. The gravitational acceleration g can be 9.8m / s 2 The gradient θ(t) can be determined based on the latitude and longitude information and elevation information in the driving path information, such as Figure 6 as shown in the example.

[0105] The traveling load prediction unit 54 predicts the traveling horsepower P serving as the minimum value using the following expressions (f23) and (f24): drv_min (t) and the driving horsepower P used as the maximum value drv_max (t).

[0106] P drv_min (t) = F drv_min (t)*(v(t)+dv_min(x))…(f23)

[0107] P drv_max (t) = F drv_max (t)*(v(t)+dv_max(x))…(f24)

[0108] The energy prediction unit 55 calculates the driving system efficiency. The energy prediction unit 55 calculates the minimum and maximum values ​​of the required energy amount as the variation range of the required energy amount. The calculation of the energy prediction value using the driving load and the driving horsepower is similar to the reference Figure 7 , Figure 8 , Fig. 9 and Fig.10 Therefore, the description thereof is omitted.

[0109] In step S205 after step S204, the information display unit 57 notifies the user of information related to the energy prediction result. The notification method is not particularly limited. The user may be notified by using an application or the like.

[0110] [Additional Notes]

[0111] Additional remarks 1 to 7 described below can be arbitrarily combined as long as no technical inconsistency occurs.

[0112] [Additional Note 1]

[0113] An energy prediction device includes: a generation unit, which generates vehicle speed prediction information by predicting the vehicle speed change state on the expected driving path to be traveled by the vehicle; a prediction unit, which uses the vehicle speed prediction information to predict the required energy required for the vehicle to travel on the expected driving path; and an acquisition unit, which acquires change information that affects the prediction of the required energy, wherein the prediction unit predicts the required energy while reflecting the change information.

[0114] The generation unit in Additional Note 1 corresponds to the vehicle speed pattern estimation unit 52 according to the present embodiment. The prediction unit in Additional Note 1 corresponds to the driving load prediction unit 54 and the energy prediction unit 55 according to the present embodiment. The acquisition unit in Additional Note 1 corresponds to the vehicle speed characteristic storage unit 41 and the change factor storage unit 44 according to the present embodiment. When the prediction unit predicts the required energy, the change information only needs to be in an available state and can be acquired by any one of the vehicle speed pattern estimation unit 52, the driving load prediction unit 54 and the energy prediction unit 55 by some means, without having to be acquired through the vehicle speed characteristic storage unit 41 and the change factor storage unit 44.

[0115] As a result of Additional Remark 1, the required energy is predicted using the change information that affects the prediction of the required energy reflected in the prediction. Therefore, the prediction of the required energy can be performed based on various states.

[0116] [Additional Note 2]

[0117] In the energy prediction device described in Additional Note 1, the above-mentioned change information includes information for identifying influencing factors affecting the prediction of required energy and their occurrence probabilities; and the above-mentioned prediction unit predicts the required energy as the expected value of energy based on the occurrence probability of the above-mentioned influencing factors.

[0118] As a result of Additional Note 2, the required energy can be predicted as an expected value. The prediction of the required energy can be performed based on various states.

[0119] [Additional Note 3]

[0120] In the energy prediction device described in Additional Note 1, the above-mentioned change information includes information identifying the influencing factors affecting the prediction of the required energy and their change ranges; and the above-mentioned prediction unit predicts the required energy based on the change range of the above-mentioned influencing factors as the predicted value of energy.

[0121] As a result of Additional Remark 3, the required energy can be predicted as a predicted value based on the variation range. The prediction of the required energy can be performed based on various states.

[0122] [Additional Note 4]

[0123] In the energy prediction device described in Additional Note 3, the variation range is identified by the maximum and minimum values ​​of the above-mentioned influencing factors.

[0124] [Additional Note 5]

[0125] In the energy prediction device described in Additional Note 3, the variation range is identified by the maximum value and the minimum value of the range in which the probability of occurrence of the above-mentioned influencing factor exceeds the threshold value.

[0126] [Additional Note 6]

[0127] An energy prediction device includes: a processor; a non-temporary computer-readable storage medium; and a group of computer-executable instructions, wherein the computer-executable instructions are stored in the computer-readable storage medium and, when read and executed by the processor, enable the processor to implement the following functions: generate vehicle speed prediction information by predicting the vehicle speed change state on the expected driving path to be traveled by the vehicle; use the vehicle speed prediction information to predict the energy required for the vehicle when traveling on the expected driving path; and obtain change information that affects the prediction of the required energy, wherein predicting the required energy includes predicting the required energy while reflecting the change information.

[0128] As a result of Additional Note 6, the required energy is predicted using the change information that affects the prediction of the required energy reflected in the prediction. Therefore, the prediction of the required energy can be performed based on various states.

[0129] [Additional Note 7]

[0130] An energy prediction method comprises: generating vehicle speed prediction information by predicting the vehicle speed change state on the expected driving path to be traveled by the above-mentioned vehicle; using the above-mentioned vehicle speed prediction information to predict the energy required for the above-mentioned vehicle when traveling on the above-mentioned expected driving path; and obtaining the predicted change information affecting the required energy, wherein predicting the required energy includes predicting the above-mentioned required energy while reflecting the above-mentioned change information.

[0131] As a result of Additional Note 7, the required energy is predicted with the change information affecting the prediction of the required energy reflected in the prediction. Therefore, the prediction of the required energy can be performed based on various states.

[0132] The control unit and method thereof described in the present disclosure can be implemented by a special-purpose computer, which is provided by, for example, a processor and a memory, and the above-mentioned processor is programmed to provide one or more functions implemented by a computer program. Alternatively, the control unit and method thereof described in the present disclosure can be implemented by a special-purpose computer, which is provided by a processor composed of a single special-purpose hardware logic circuit or more. Alternatively, the control unit and method thereof described in the present disclosure can be implemented by a single special-purpose computer or more special-purpose computers. The special-purpose computer can be composed of a processor programmed to provide one or more functions, a memory, and a combination of a processor composed of a single hardware logic circuit or a plurality of hardware logic circuits. In addition, the computer program can be stored as an instruction executed by a computer and can be read by a computer in a non-temporary computer-readable tangible storage medium.

[0133] The present embodiment has been described above with reference to specific examples. However, the present invention is not limited to these specific embodiments. As long as the features of the present invention are included, appropriate design modifications of the specific embodiments described above by those skilled in the art are included within the scope of the present invention. The elements and their arrangements, conditions, shapes, etc. included in the specific examples described above are not limited to those given as examples, and can be appropriately modified. As long as no technical inconsistency occurs, the combination of the elements included in the specific examples described above can be appropriately changed.

Claims

1. An energy prediction device, comprising: a generating unit, the generating unit generating vehicle speed prediction information by predicting a vehicle speed change state of the vehicle on an expected driving path to be driven by the vehicle; a prediction unit, the prediction unit using the vehicle speed prediction information to predict the required energy required for the vehicle to travel on the expected travel path; as well as An acquisition unit is configured to acquire change information affecting the prediction of required energy, wherein the prediction unit predicts the required energy based on the change information.

2. The energy prediction device according to claim 1, characterized in that: The change information includes information for identifying factors affecting the prediction of the required energy and their probability of occurrence, The prediction unit predicts the required energy as an expected value of energy based on the occurrence probability of the influencing factor.

3. The energy prediction device according to claim 1, characterized in that: The change information includes information for identifying factors affecting the prediction of the required energy and their range of change, The prediction unit predicts required energy as a predicted value of energy based on a variation range of the influencing factor.

4. The energy prediction device according to claim 3, characterized in that: The variation range is identified by the maximum and minimum values ​​of the influencing factors.

5. The energy prediction device according to claim 3, characterized in that: The variation range is identified by the maximum value and the minimum value of the range in which the occurrence probability of the influencing factor exceeds the threshold value.

6. An energy prediction device, comprising: processor; a non-transitory computer-readable storage medium; as well as A set of computer executable instructions, which are stored in a computer readable storage medium and, when read and executed by the processor, cause the processor to implement the following functions: generating vehicle speed prediction information by predicting a vehicle speed change state of the vehicle on an expected driving path to be driven by the vehicle; Using the vehicle speed prediction information to predict energy required for the vehicle to travel on the expected travel path; and Obtain information on changes that affect the forecast of required energy, where: Predicting the required energy includes predicting the required energy based on the change information.

7. An energy prediction method, comprising: generating vehicle speed prediction information by predicting a vehicle speed change state of the vehicle on an expected driving path to be driven by the vehicle; Using the vehicle speed prediction information to predict energy required for the vehicle to travel on the expected travel path; and Obtain information on changes that affect the forecast of required energy, where: Predicting the required energy includes predicting the required energy based on information reflecting the change.

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