WO2013094046A1

WO2013094046A1 - Energy consumption prediction device, energy consumption prediction method, and energy consumption prediction program

Info

Publication number: WO2013094046A1
Application number: PCT/JP2011/079735
Authority: WO
Inventors: 千尋川端; 和俊北野
Original assignee: パイオニア株式会社
Priority date: 2011-12-21
Filing date: 2011-12-21
Publication date: 2013-06-27
Also published as: JPWO2013094046A1; JP5687363B2

Abstract

This energy consumption prediction device (100) comprises: a scheduled travel route calculating unit (101) that calculates a scheduled travel route corresponding to a destination; a speed calculating unit (102) that calculates speed information, which indicates the speed on the scheduled travel route, for a plurality of conditions; a travel resistance value calculating unit (103) that calculates a travel resistance value on the basis of the speed information; a driving force calculating unit (104) that calculates the driving force of a vehicle on the basis of the travel resistance value; a torque calculating unit (105) that calculates torque information indicating the torque for the scheduled travel route on the basis of the driving force; an efficiency map acquiring unit (106) that acquires an efficiency map of a motor which drives the vehicle; an energy consumption calculating unit (107) that calculates energy consumption on the basis of the speed information, the torque information, and the efficiency map for the scheduled travel route; and a selection unit (110) that selects, from the energy consumptions calculated for respective pieces of speed information for said plurality of conditions, the greatest energy consumption as the energy consumption for the scheduled travel route.

Description

Energy consumption prediction apparatus, energy consumption prediction method, and energy consumption prediction program

The present invention relates to an energy consumption prediction apparatus, an energy consumption prediction method, and an energy consumption prediction program for supplying power to a motor of a vehicle to drive the vehicle. However, the use of the present invention is not limited to the above-described consumption energy prediction apparatus, consumption energy prediction method, and consumption energy prediction program.

Conventionally, an electric vehicle (EV) that is a moving body is provided with a motor and a wheel is driven, and an in-wheel motor structure is provided in which the motor is provided on the wheel. Is disclosed.

For example, when estimating the energy consumption of a motor on a planned travel route, there is a technique for estimating the amount of energy consumption in consideration of travel resistance and other resistances that occur in a traveling vehicle (for example, see Patent Document 1 below). .) In addition, there is a technique for estimating a power saving route and power consumption using a vehicle speed and a road gradient (see, for example, Patent Document 2 below).

JP 2009-67350 A JP 2011-122926 A

However, in any of the above technologies, the efficiency of the motor provided in the vehicle is not taken into consideration, and the efficiency varies from motor to motor. It is considered that there is a problem that the battery runs out because it is different from the estimation after traveling.

In order to solve the above-described problems and achieve the object, the energy consumption predicting apparatus according to the present invention is an energy consumption predicting apparatus that predicts energy consumed when a vehicle travels on a planned travel route to a destination. A planned travel route calculating means for calculating the planned travel route corresponding to the destination, a speed calculating means for calculating speed information indicating a speed on the planned travel route for a plurality of conditions, and speeds for the plurality of conditions For each piece of information, based on the driving force, a driving resistance value calculating unit that calculates a driving resistance value based on the speed information, a driving force calculating unit that calculates a driving force of the vehicle based on the driving resistance value, and A torque calculation means for calculating torque information indicating the torque of the planned travel route, and an efficiency map acquisition for obtaining an efficiency map of the drive means for driving the vehicle. Energy consumption calculating means for calculating the energy consumption based on the speed information, the torque information, and the efficiency map in the planned travel route, and the energy consumption calculated for each of the speed information of the plurality of conditions. Selecting means for selecting the largest consumed energy as the consumed energy in the scheduled travel route.

The energy consumption prediction method according to the present invention is a consumption energy prediction method of a consumption energy prediction device for predicting consumption energy consumed when a vehicle travels on a planned travel route to a destination. For each of the planned travel route calculation step for calculating the corresponding planned travel route, the speed calculation step for calculating speed information indicating the speed in the planned travel route for a plurality of conditions, and the speed information for the plurality of conditions, A travel resistance value calculating step of calculating a travel resistance value based on the speed information; a driving force calculation step of calculating a driving force of the vehicle based on the travel resistance value; and A torque calculation step of calculating torque information indicating torque, an efficiency map acquisition step of acquiring an efficiency map of a driving means for driving the vehicle, Based on the speed information, the torque information, and the efficiency map on the planned travel route, among the consumed energy calculated step for calculating the consumed energy and the consumed energy calculated for each of the speed information of the plurality of conditions, And a selection step of selecting large energy consumption as energy consumption in the planned travel route.

An energy consumption prediction program according to the present invention is an energy consumption prediction program applied to an energy consumption prediction apparatus for predicting energy consumption consumed when a vehicle travels on a planned travel route to a destination, and is a computer. A planned travel route calculating means for calculating the planned travel route corresponding to the destination, a speed calculating means for calculating speed information indicating speed on the planned travel route for a plurality of conditions, and a speed for the plurality of conditions For each piece of information, based on the driving force, a driving resistance value calculating unit that calculates a driving resistance value based on the speed information, a driving force calculating unit that calculates a driving force of the vehicle based on the driving resistance value, and Torque calculating means for calculating torque information indicating the torque of the planned travel route, and an efficiency map of the driving means for driving the vehicle. Efficiency map acquisition means for acquiring a power consumption, energy consumption calculation means for calculating the energy consumption based on the speed information, the torque information, and the efficiency map in the planned travel route, speed information of the plurality of conditions It functions as a selection means for selecting the largest consumed energy among the consumed energy calculated for each as the consumed energy in the planned travel route.

FIG. 1 is a block diagram illustrating a configuration of the energy consumption prediction apparatus according to the embodiment. FIG. 2 is a flowchart illustrating the processing contents of the energy consumption prediction apparatus according to the embodiment. FIG. 3 is a block diagram illustrating a hardware configuration of the energy consumption prediction apparatus. FIG. 4 is a diagram for explaining the outline of speed prediction. FIG. 5 is a chart showing speed changes in a certain section. FIG. 6A is a diagram for explaining speed calculation for each condition. FIG. 6B is a flowchart of the speed calculation process for each condition. FIG. 7A is a flowchart illustrating the detailed contents of speed prediction. FIG. 7-2 is a chart illustrating an example of speed prediction. FIG. 8 is a chart showing an example of position calculation calculated based on speed prediction. FIG. 9 is a chart showing a gradient calculation example calculated based on the position change. FIG. 10 is a diagram illustrating a configuration example of the driving force observer. FIG. 11 is a chart showing a motor efficiency map. FIG. 12 is a chart showing the energy consumption after calculation.

(Embodiment)
Exemplary embodiments of a consumption energy prediction apparatus, a consumption energy prediction method, and a consumption energy prediction program according to the present invention will be described below in detail with reference to the accompanying drawings. In the following description, an in-wheel type configuration in which a motor is provided for each wheel of the vehicle will be described as an example. However, the present invention is for predicting energy consumption of a vehicle having a motor, and the motor is an in-wheel motor. It is applicable not only to.

(Configuration of energy consumption prediction device)
FIG. 1 is a block diagram illustrating a configuration of the energy consumption prediction apparatus according to the embodiment. The energy consumption prediction apparatus 100 predicts energy consumption consumed when a vehicle travels on a planned travel route to a destination. The energy consumption prediction apparatus 100 includes a planned travel route calculation unit 101, a speed calculation unit 102, a travel resistance value calculation unit 103, a driving force calculation unit 104, a torque calculation unit 105, an efficiency map acquisition unit 106, A consumption energy calculation unit 107, a gradient acquisition unit 108 that acquires gradient information indicating a gradient in the planned travel route, a condition setting unit 109, and a selection unit 110 are provided.

The planned travel route calculation unit 101 calculates the planned travel route to the destination based on the input of the destination of the vehicle. The speed calculation unit 102 calculates speed information indicating the speed on the planned travel route. The running resistance value calculation unit 103 calculates a running resistance value based on the speed information. The driving force calculation unit 104 calculates the driving force of the vehicle based on the running resistance value. The torque calculation unit 105 calculates torque information indicating the torque of the planned travel route based on the driving force.

The efficiency map acquisition unit 106 acquires a driving means for driving the vehicle, that is, an efficiency map of the motor. The efficiency map is a map for obtaining the efficiency of the motor from the torque and the speed, and has a characteristic specific to the motor of the vehicle. The energy consumption calculation unit 107 calculates energy consumption based on speed information, torque information, and an efficiency map on the planned travel route.

Also, the running resistance value calculation unit 103 can calculate the running resistance value based on the speed information calculated by the speed calculation unit 102 and the gradient information acquired by the gradient acquisition unit 108.

Then, the condition setting unit 109 causes the speed calculation unit 102 to calculate speed information indicating the speed on the planned travel route for a plurality of conditions. The multiple conditions are conditions for calculating different values of energy consumption due to differences in the driving state of the vehicle.In the example shown in the figure, the speed change state of the vehicle is the same even for the same planned driving route. Different conditions, for example, a case where each signal on the planned travel route passes without stopping (non-stop) and a case where each signal stops (stop) are different conditions. This condition is set in the condition setting unit 109 in advance.

Due to the different speed conditions, the running resistance, driving force, and torque calculated by the speed calculating unit 102, the running resistance value calculating unit 103, the driving force calculating unit 104, and the torque calculating unit 105 are different depending on the conditions. . As a result, the consumed energy calculation unit 107 calculates consumed energy according to conditions.

The selection unit 110 selects the largest consumed energy as the consumed energy in the planned travel route among the consumed energy calculated for each of the speed information of a plurality of conditions. Information on the selected energy consumption is displayed on a display unit (not shown) or output to the outside.

FIG. 2 is a flowchart showing the processing contents of the energy consumption prediction apparatus according to the embodiment. The energy consumption prediction apparatus 100 predicts energy consumption in the following processing order. First, the planned travel route calculation unit 101 searches for a route (planned route) to the destination based on the input of the destination of the vehicle (step S201). Map information is used for this search.

Next, the speed calculation unit 102 predicts speed information indicating the speed on the planned travel route according to the conditions set by the condition setting unit 109 (step S202). At this time, the position and distance of the vehicle for each hour on the planned route can be obtained based on the speed calculated for each condition (step S203). As described above, for example, the conditions are different when each signal on the planned travel route passes without stopping (non-stop) and when it stops at each signal (stop).

In the following processing, the energy consumption at this position is calculated for each condition based on the position of the vehicle that varies with time. First, based on the position of the vehicle on the planned route, the gradient acquisition unit 108 calculates the gradient at this position from the map information (step S204). Then, the travel resistance value calculation unit 103 calculates a travel resistance value based on the calculated speed information and gradient information (step S205). Thereafter, the driving force calculation unit 104 calculates the driving force of the vehicle based on the calculated running resistance value (step S206).

Next, the torque calculation unit 105 calculates a necessary torque at this position based on the calculated driving force (step S207). Thereafter, the efficiency map acquisition unit 106 acquires an efficiency map of the vehicle motor, and calculates the efficiency at this position using the efficiency map based on the calculated speed and torque (step S208).

Thereafter, the consumed energy calculation unit 107 calculates consumed energy based on the speed information, torque information, and efficiency map at this position (step S209). The calculated energy consumption information is displayed on a display unit (not shown) or output to the outside. The processing after step S204 is repeatedly executed for each position of the planned route. For example, the planned route is divided into predetermined sections (for example, a node-node link (edge)), and energy consumption is calculated for each section.

Each process of step S202 to step S209 is calculated for each condition set in the condition setting unit 109. Then, the selection unit 110 determines whether the calculation based on all conditions has been completed (step S210). If calculation based on conditions still remains (step S210: No), the process returns to step S202, and if calculation based on all conditions is completed (step S210: Yes), the process proceeds to step S211.

In step S211, the selection unit 110 selects and outputs the largest consumption energy as the consumption energy in the planned travel route among the consumption energy calculated for each of the speed information of the plurality of conditions calculated by the consumption energy calculation unit 107. (Step S211).

According to the above configuration, in consideration of the characteristics of the motor mounted on the vehicle, the torque on the planned route is calculated, and the efficiency of the motor of the vehicle is obtained from the torque and speed. The energy consumption of the planned route can be predicted. As a result, the energy consumed before the vehicle travels can be accurately predicted.

And because it is configured to predict the energy consumption according to speed conditions and select the largest energy consumption as the energy consumption in the planned driving route, it prevents the actual energy consumption during vehicle travel from exceeding the predicted energy consumption Will be able to. As a result, it is possible to avoid problems such as the vehicle running out of battery along the travel route.

(Hardware configuration of energy consumption prediction device)
Next, examples of the above embodiment will be described. FIG. 3 is a block diagram illustrating a hardware configuration example of the energy consumption prediction apparatus. 3, the energy consumption prediction apparatus 100 includes a CPU 301, a ROM 302, a RAM 303, a communication I / F 304, a GPS unit 305, and various sensors 306. Each component 301 to 306 is connected by a bus 307.

The CPU 301 governs overall control of the energy consumption prediction apparatus 100. The ROM 302 records and holds programs such as a boot program and an energy prediction program. The RAM 303 is used as a work area for the CPU 301. That is, the CPU 301 executes the program recorded in the ROM 302 while using the RAM 303 as a work area.

The communication I / F 304 is connected to the network via wireless and functions as an interface between the energy consumption prediction apparatus 100 and the CPU 301. The communication network functioning as a network includes a public line network, a mobile phone network, DSRC (Dedicated Short Range Communication), LAN, WAN, and the like. The communication I / F 304 is, for example, a public line connection module, an ETC unit, an FM tuner, a VICS (Vehicle Information and Communication System (registered trademark)) / beacon receiver, or the like.

The GPS unit 305 receives radio waves from GPS satellites and outputs information indicating the current position of the vehicle. The output information of the GPS unit 305 is used when the CPU 301 calculates the current position of the vehicle together with output values of various sensors such as a speed sensor, an acceleration sensor, and a yaw rate sensor. The information indicating the current position is information for specifying one point on the map data, such as latitude / longitude and altitude.

Each configuration shown in FIG. 1 realizes its function by the CPU 301 executing a predetermined program using programs and data recorded in the ROM 302, RAM 303, and the like. And the energy consumption prediction apparatus 100 shown in FIG. 3 can be made into one function of a navigation apparatus.

(About speed prediction)
FIG. 4 is a diagram for explaining the outline of speed prediction. The process of step S202 will be described. The figure shows a planned travel route of the vehicle. A predetermined route (scheduled travel route) 401 starting from the departure point (S) and returning to the destination (G, the same position as the departure point in the illustrated example) is shown. An example is shown.

1. First, in the speed prediction, a route is divided into several sections based on map information, and a graph using feature points such as a signal p and a corner as a node is used. In addition, the edge (link) between the nodes has distance information for each edge, speed information, presence / absence of a signal, presence / absence of a corner, etc. as information necessary for speed prediction. Further, it is possible to have a configuration in which each edge has rolling resistance coefficient and gradient information for each edge. These can be acquired not only from sensor detection but also from map information.

2. Next, the traveling speed for each section is predicted based on the edge information. At this time, the traveling speed is determined according to a predetermined rule. For example,
a. The acceleration / deceleration is 0.1 [G].
b. When there is a signal at the edge of the corner, the speed at that point is set to 0 [km / h].
c. When the edge end is a corner and there is no signal, the speed at that point is set to 10 [km / h].
d. If the edge is not a corner but a signal, the speed is set to 0 [km / h].
Etc.
Moreover, in speed prediction, not only the above-described rule is used, but also probe data obtained by actually traveling the vehicle can be used.

3. Next, the speed for each section is predicted based on the edge information. FIG. 5 is a chart showing speed changes in a certain section. In the figure, Vmax is a speed based on edge information (upper limit speed), Vstart which is the start point is the end speed in the previous section, a is the acceleration determined based on the rule, and -a is based on the rule. The determined deceleration, Vstop, is the terminal speed in this section determined based on the rule.

Each time in this one section can be obtained by the following formula.
Acceleration time ta = (Vmax−Vstart) / a
Deceleration time td = (Vmax−Vstop) / a
Constant speed travel time tc = [X − ((Vstart · ta) / 2) + ((Vstop · td) / 2) / Vmax} − [(ta / 2) + (td / 2)]
(However, X is the distance of this section)

(About speed prediction according to conditions)
The speed calculation unit 102 described above calculates a speed for each condition set in the condition setting unit 109. The plurality of conditions will be described. FIG. 6A is a diagram for explaining speed calculation for each condition. (A) shows an outline of a route (scheduled route) 401 searched by the planned traveling route calculation unit 101. It is assumed that there is a straight signal p1 and a turn signal p2 on the planned route 401.

As shown in (b), the planned route 401 is configured by a graph (link) 601a having feature points such as intersections, signals, and corners as nodes. The condition 1 is set such that the vehicle stops at the turn signal p2 and does not stop at the straight signal p1 other than the turn (non-stop). In this case, a graph (link) 601b ignoring the straight signal p1 is reconstructed, and the speed based on the link 601b is calculated. The time-speed chart 610 shown in the figure corresponds to this condition 1.

(Condition 2) A case (stop) in which all signals p1 and p2 are stopped is set. In this case, the speed based on the graph 601a indicating all the feature points (signals p1, p2) of the planned route 401 is calculated.

In this way, in the planned route until the vehicle reaches the destination, speed changes occur due to signals, intersections, turning corners, etc., and torque, driving force also changes in response to this speed change, The amount of energy consumed will change. In this way, by setting the conditions assumed when the vehicle is traveling in advance and calculating the speed for each condition, the predicted energy consumption can be brought close to the actual energy consumption. Specifically, as will be described later, this is realized by the selection of energy consumption by condition by the selection unit 110.

FIG. 6-2 is a flowchart showing speed calculation processing according to conditions. The configuration and reconstruction of the above-mentioned graphs according to conditions are mainly described. The details of the process of step S202 of FIG. 2 are shown. First, the speed calculation unit 102 configures a graph in which feature points such as intersections, signals, and corners are nodes for the travel route calculated by the planned travel route calculation unit 101 (step S601).

Next, the speed calculation unit 102 calculates the speed for each condition set in the condition setting unit 109. In this speed calculation, the condition setting unit 109 receives the above conditions 1. non-stop; 2. Assume that stop is set. Accordingly, the speed calculation unit 102 determines which condition is to be calculated. For example, if the condition is 2. If it is a stop (step S602: Yes), the speed is predicted based on the graph constructed in step S601 (step S609).

On the other hand, the condition is 1. If it is non-stop (step S602: No), the following processing is performed. First, it moves to the first node of the travel route (step S603), and determines whether it is a destination (step S604). If this node is the destination (step S604: No), the process proceeds to step S609. If this node is not the destination (step S604: Yes), it is determined whether the next node on the travel route is a corner or a destination (step S605).

If the next node of the travel route is not at the corner or the destination (step S605: Yes), the next node is ignored and the previous node is joined to the next node (step S606). Then, the speed information between nodes is changed (step S607), and the process returns to step S605. Since the distance between the nodes becomes longer due to the combination of the nodes, the acceleration / deceleration of the vehicle is correspondingly reduced by one and the speed between the nodes changes accordingly.

In step S605, if the next node on the travel route is a corner or a destination (step S605: No), it moves to the next node on the travel route (step S608), and returns to step S604.

In the above example, after constructing the graph with condition 2, the process for reconstructing the graph for condition 1 is performed, and condition 1.2. The speed is predicted based on the graph composed of each.

Fig. 7-1 is a flowchart showing the detailed contents of speed prediction. A detailed processing example of step S609 in FIG. 6-2 will be described. First, it moves to the first node in the section (step S701). Then, it is determined whether this node is not the destination (step S702). If it is the destination (step S702: No), the process is terminated. If it is not the destination (step S702: Yes), the following process is performed. Do.

First, the distance to the next node is calculated based on the map information or the like (step S703). Then, the acceleration time ta, the deceleration time td, and the constant speed traveling time tc between the nodes are calculated from the distance / speed information between the nodes and the set acceleration / deceleration (step S704). Thereafter, the speed between the nodes is calculated from the calculated acceleration time ta, deceleration time td, constant speed traveling time tc, speed information between the nodes, and the set acceleration / deceleration (step S705). Thereafter, the node moves to the next node (step S706), and returns to step S702.

Fig. 7-2 is a chart showing an example of speed prediction. The horizontal axis is distance, and the vertical axis is speed. The change of the speed for every time in the driving | running route 401 of FIG. 4 is shown. This figure shows the predicted speed when the signal other than the turn by condition 1 does not stop (non-stop) and the predicted speed when the signal stops by condition 2 (stop) (one-dot chain line in the figure). ing. In the case of stopping at a signal (stop), it is shown that the speed changes more finely in addition to the change characteristics when the signal does not stop at a signal other than a corner (non-stop).

(About position and gradient calculation)
Next, position calculation and gradient calculation will be described. For position calculation, position changes are calculated in time series based on the speed prediction. The position calculation is performed based on the following formula.

FIG. 8 is a chart showing an example of position calculation calculated based on speed prediction. The horizontal axis is time, and the vertical axis is distance. The example calculated based on the prediction speed (stop) of FIG. 7 is shown. As shown in the figure, the distance is integrated every time.

Next, gradient calculation calculates time-series gradient change from position change. FIG. 9 is a chart showing a gradient calculation example calculated based on the position change. The horizontal axis is time, and the vertical axis is gradient (angle). This gradient θ [deg] can be obtained from the map information, and the gradient for each position is obtained. The map information is stored in, for example, the ROM 302 or the external storage unit illustrated in FIG. Or it can acquire from an external server etc. via communication I / F304.

(About running resistance / driving force calculation, torque calculation)
Next, traveling resistance and driving force calculation will be described. The running resistance is calculated based on the above speed and gradient. The driving force is calculated from this running resistance. Driving force = air resistance + rolling resistance + gradient resistance + acceleration resistance + internal resistance, and can be calculated based on the following formula.
Fd [N] = 1/2 · ρC _D Av ² + μrmg + mg sin θ + m (dv / dt) + Ri
(Fd: driving force, [rho: air density, C _{_D:} C _D value, A: front projected area, .mu.r: rolling resistance coefficient, m: vehicle weight, g: gravitational acceleration, Ri: Internal resistance)
The internal resistance is a resistance component other than air resistance, rolling resistance, gradient resistance, and acceleration resistance, including mechanical loss of the drive system, and is assumed to be a known one here.

The torque is calculated from the above driving force. FIG. 10 is a diagram illustrating a configuration example of the driving force observer. Here, the torque T can be obtained by calculating back and forth the input / output of the driving force observer 1000 having the configuration shown in the figure. That is,
T = J · (dω / dt) + rFd
Can be obtained. The torque T is the total torque per vehicle.
(Where T: torque [Nm], J: tire inertia [Nms ² ], ω: tire rotation speed [rad / s], r: tire radius [m])

As numerical examples of the above parameters, for example, ρ = 1.29 [kg / m ³ ], C _D = 0.530, A = 1.28 [m ² ], μr = 0.0185, m = 395 [ kg].

(About efficiency calculation)
FIG. 11 is a chart showing a motor efficiency map. This efficiency map 1100 shows the relationship between the rotational speed on the horizontal axis and the torque on the vertical axis and the efficiency. The efficiency map 1100 has different characteristics for each motor mounted on the vehicle. For example, the efficiency map 1100 is stored in advance in the ROM 302 or the external storage unit illustrated in FIG. Or it can acquire from an external server etc. via communication I / F304. By using this efficiency map 1100, the efficiency η can be obtained from the intersection corresponding to the rotational speed and the torque.

The efficiency η can be expressed by an approximate expression as a function relating to torque and rotational speed as follows.
η = f (T, ω)

The torque T is the total torque for each vehicle, and when the vehicle includes one motor, one efficiency map 1100 is used. When the vehicle has a plurality of motors each having an in-wheel motor on each wheel, the torque is different for each wheel. For example, when an in-wheel motor is provided for each of the four wheels, the total torque is simply divided by four to drive each motor, but the torque distribution of each wheel differs depending on the curve, gradient, and the like. For this reason, a vehicle equipped with such a plurality of motors is provided with an efficiency map 1100 for the number of motors, resulting in an independent torque for each motor of each wheel, and correspondingly different efficiency.

(About energy consumption calculation)
The consumed energy is calculated based on the speed, torque, and efficiency after each calculation described above. The consumed energy E is calculated by the following formula.

FIG. 12 is a chart showing the energy consumption after calculation. The horizontal axis represents the distance, and the vertical axis represents the amount of electric power. The integrated value of the amount of energy consumed when the travel route 401 from the starting point to the destination shown in FIG. 4 is traced is shown. The figure shows the calculation results of energy consumption under the condition of stopping at each signal (stop) and the condition of not stopping at a signal other than a corner (non-stop) set as speed conditions. The calculated energy consumption amount can be output as an integrated amount as shown in FIG. 12 on a display unit or the like as a graph or numerical value, or output outside the apparatus.

Here, the selection unit 110 always selects and outputs the largest value for the consumed energy calculated according to the conditions. In this case, the energy consumption shown in FIG. 12 is any of the energy consumption at each distance among the conditions of stopping at each signal (stop) and the energy consumption of not stopping at a signal other than a corner (non-stop). The energy consumption of the higher one (always above the figure) is selected.

According to the result shown in FIG. 12, it becomes possible to easily know the tendency and extent of the increase in the amount of energy consumption and the final amount of energy consumed before reaching the destination.

Particularly, by selecting a configuration in which the selection unit 110 always selects high energy consumption, the estimated energy consumption at each distance is always estimated to be higher than the actual energy consumption. As a result, the actual amount of energy consumed does not exceed the predicted amount of energy consumed during actual traveling, and problems such as the inability to travel due to insufficient remaining battery power can be avoided.

In the above embodiment, the vehicle is provided with all the functions of the energy consumption estimation device. However, the present invention is not limited to this. For example, the configuration shown in FIG. 1 and FIG. 3, that is, the configuration relating to the energy consumption estimation may be provided in an external server, and the vehicle may be configured to include a terminal having only a function of communicating with the server. In this case, the server executes processing related to the energy consumption estimation, and the terminal of the vehicle is configured so that the server can detect information that can identify the motor mounted on the vehicle, such as the communication function with the server and the type of the vehicle. do it. With such a configuration, the apparatus cost and processing burden on the vehicle side can be reduced.

As described above, by using information related to the planned route of the vehicle and an efficiency map having characteristics specific to the motor of the vehicle, the amount of energy consumed to reach the destination can be accurately measured before the vehicle travels. Be able to predict. As a result, an appropriate charging point on the planned route can be planned in advance.

Note that the method described in this embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. This program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. The program may be a transmission medium that can be distributed via a network such as the Internet.

DESCRIPTION OF SYMBOLS 100 Consumption energy prediction apparatus 101 Planned travel route calculation part 102 Speed calculation part 103 Traveling resistance value calculation part 104 Driving force calculation part 105 Torque calculation part 106 Efficiency map acquisition part 107 Consumption energy calculation part 108 Gradient acquisition part 109 Condition setting part 110 Selection 305 GPS unit 306 Various sensors 307 Bus 401 Traveling route 1000 Driving force observer 1100 Efficiency map

Claims

An energy consumption prediction device for predicting energy consumption consumed when a vehicle travels on a planned travel route to a destination,
A planned travel route calculation means for calculating the planned travel route corresponding to the destination;
Speed calculating means for calculating speed information indicating speed on the planned travel route for a plurality of conditions;
For each of the multiple conditions of speed information,
Running resistance value calculating means for calculating a running resistance value based on the speed information;
Driving force calculating means for calculating the driving force of the vehicle based on the running resistance value;
Torque calculating means for calculating torque information indicating the torque of the planned travel route based on the driving force;
Efficiency map acquisition means for acquiring an efficiency map of drive means for driving the vehicle;
Energy consumption calculating means for calculating the energy consumption based on the speed information, the torque information, and the efficiency map in the planned travel route;
Selecting means for selecting the largest consumed energy as the consumed energy in the planned travel route among the consumed energy calculated for each of the speed information of the plurality of conditions;
A device for predicting energy consumption, comprising:
2. The energy consumption prediction apparatus according to claim 1, wherein the plurality of conditions include a case where no stop is made at a plurality of nodes of the planned travel route and a case where a stop is made at a part of the plurality of nodes.
The energy consumption prediction apparatus according to claim 2, wherein the node is an intersection, a signal, or a turn on the planned travel route.
Further comprising gradient acquisition means for acquiring gradient information indicating the gradient in the planned travel route;
The said running resistance value calculation means calculates the said running resistance value based on the said speed information and the said gradient information, The consumption energy prediction apparatus of Claim 1 characterized by the above-mentioned.
The torque calculation means includes
When the torque is T, the driving force is Fd, the moment of inertia of a wheel mounted on the vehicle is J, the angular velocity of the wheel is ω, the radius of the wheel is r, and the elapsed time is t,
T = J · (dω / dt) + rFd
The energy consumption prediction apparatus according to claim 1, wherein a change in the torque is calculated by the following.
A consumption energy prediction method of a consumption energy prediction device that predicts consumption energy consumed when a vehicle travels on a planned travel route to a destination,
A planned travel route calculating step for calculating the planned travel route corresponding to the destination;
A speed calculating step of calculating speed information indicating speed on the planned travel route with respect to a plurality of conditions;
For each of the multiple conditions of speed information,
A running resistance value calculating step of calculating a running resistance value based on the speed information;
A driving force calculation step of calculating the driving force of the vehicle based on the running resistance value;
A torque calculating step of calculating torque information indicating the torque of the planned travel route based on the driving force;
An efficiency map acquisition step of acquiring an efficiency map of driving means for driving the vehicle;
An energy consumption calculating step of calculating the energy consumption based on the speed information, the torque information, and the efficiency map in the planned travel route;
A selection step of selecting the largest consumed energy as the consumed energy in the planned travel route among the consumed energy calculated for each of the speed information of the plurality of conditions,
A method for predicting energy consumption, comprising:
An energy consumption prediction program applied to an energy consumption prediction device for predicting energy consumption consumed when a vehicle travels on a planned travel route to a destination,
Computer
A planned travel route calculation means for calculating the planned travel route corresponding to the destination;
Speed calculating means for calculating speed information indicating speed on the planned travel route for a plurality of conditions;
For each of the multiple conditions of speed information,
Running resistance value calculating means for calculating a running resistance value based on the speed information;
Driving force calculating means for calculating the driving force of the vehicle based on the running resistance value;
Torque calculating means for calculating torque information indicating the torque of the planned travel route based on the driving force;
Efficiency map acquisition means for acquiring an efficiency map of drive means for driving the vehicle;
Energy consumption calculating means for calculating the energy consumption based on the speed information, the torque information, and the efficiency map in the planned travel route;
Selection means for selecting the largest consumed energy as the consumed energy in the planned travel route among the consumed energy calculated for each of the speed information of the plurality of conditions,
Energy consumption prediction program characterized by functioning as