JP2001183150A - Running pattern generator - Google Patents

Abstract

(57) [Summary] [Problem] To provide a traveling pattern generation device capable of generating a strict energy balance schedule. A road information acquisition means for acquiring information about a planned traveling route of a host vehicle, and a traveling pattern generation device for generating a traveling pattern of the own vehicle on the planned traveling route using information acquired by the road information acquiring means. 1, and includes a number-of-stops estimating unit 13 for estimating the number of stops of the host vehicle on the planned traveling route using the information acquired by the road information acquiring unit 11.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a travel pattern generating apparatus for automatically predicting a travel pattern on a planned route using information on a planned route of a vehicle.

[0002]

2. Description of the Related Art As an example of this kind of running pattern generating apparatus, there is known an auxiliary drive control apparatus for an internal combustion engine described in Japanese Patent Application Laid-Open No. 9-324665. In this method, energy storage and release schedules are determined by classifying routes to be traveled by road types such as “highway” and “urban area” and predicting travel speed in each section.

[0003]

However, the conventional apparatus does not sufficiently consider the stop and start, which have a large effect on the energy balance, in the section classified by the road type, so that the stop required for actual traveling is not considered. In addition, the energy balance associated with the start is omitted.

[0004] For this reason, there has been a problem that strict energy accumulation and release scheduling cannot be performed in consideration of regeneration of deceleration energy at the time of stopping and driving force assist for acceleration at the time of starting.

[0005] The present invention has been made in view of such problems of the prior art, and predicts the number of stops and a stop position on a scheduled traveling route to generate a more precise energy balance schedule. It is an object to provide a pattern generation device.

[0006]

(1) In order to achieve the above object, a traveling pattern generation device according to claim 1 has road information acquisition means for acquiring information on a planned traveling route of a vehicle. In a travel pattern generation device that generates a travel pattern of the own vehicle on a scheduled travel route using information acquired by the road information acquisition unit, the own vehicle on a scheduled travel route using information acquired by the road information acquisition unit And a stop number estimating means for estimating the stop number of times.

[0007] The traveling pattern generating device according to claim 2 further comprises traveling speed prediction means for predicting the traveling speed of the own vehicle on the planned traveling route using the information acquired by the road information acquisition means. It is characterized by.

Further, the traveling pattern generating device according to a third aspect of the present invention further comprises acceleration / deceleration prediction means for predicting the acceleration / deceleration of the own vehicle on the scheduled traveling route using the information acquired by the road information acquisition means. It is characterized by.

Further, according to a fourth aspect of the present invention, there is provided the traveling pattern generating apparatus, wherein the number of stops predicted by the number-of-stops prediction means, the traveling speed predicted by the traveling speed prediction means, and the acceleration / deceleration predicted by the acceleration / deceleration prediction means. The vehicle further includes a traveling pattern generation unit that generates a traveling pattern of the host vehicle based on the speed.

In the running pattern generating apparatus according to the first to fourth aspects, not only the speed and acceleration but also the number of stops are predicted using the road information on the planned traveling route.
In particular, in a vehicle equipped with energy storage means such as a hybrid vehicle, the running pattern takes into account the energy balance required for the actual stoppage and the start and stop, and more accurate energy storage and release scheduling can be performed.

(2) The prediction method in the number-of-stops prediction means of the present invention can adopt various modes.

In the running pattern generating apparatus according to the fifth aspect, the number-of-stops estimating means detects a position of an intersection and a traffic signal on a planned traveling route of the vehicle acquired by the road information acquiring means. Means,
The scheduled travel route of the vehicle is divided into several sections, an average interval between intersections and traffic signals present in each section is obtained, and the number of stops of the own vehicle in each section is predicted based on the average interval. 2 stop number prediction means.

According to the fifth aspect of the present invention, the planned traveling route is divided into several sections according to the road type and the large intersection, and the number of stops is predicted from the average interval between the intersections and the traffic signals existing in the divided sections. And the number of stops according to the characteristics of the road can be predicted.

Further, in the running pattern generating device according to the present invention, the second stop number predicting means varies the number of stops according to a running time zone.

According to the sixth aspect of the invention, the predicted value of the number of stops is increased or decreased according to the time zone in which the own vehicle passes through the scheduled travel route. For example, it is possible to predict the number of stops adapted to the actual traffic conditions, such as reducing the number of stops.

Further, in the running pattern generating apparatus according to the present invention, the second stop number predicting means predicts that the vehicle will stop at an intersection where the vehicle turns in a direction crossing an oncoming lane on the planned traveling route.

According to the present invention, the vehicle is predicted to stop at an intersection where the host vehicle turns in a direction crossing the oncoming lane on the scheduled traveling route, for example, at an intersection where the vehicle turns left when traveling on the left and turns left when traveling on the right. In addition, it is possible to predict a stopping place that is more suitable for actual driving.

In the running pattern generating apparatus according to the present invention, the second stop number predicting means may make a right or left turn to a road having a higher priority than the own vehicle on the planned traveling route or a road having a higher priority than the own vehicle. At intersections crossing, we expect to stop.

According to the eighth aspect of the present invention, since the vehicle is predicted to turn right or left to a road having a higher priority than the road on which the vehicle is traveling on the planned traveling route, or to stop at an intersection crossing the road having a high priority. It is possible to predict a stopping place that is more suitable for actual driving.

Further, in the running pattern generation device according to the ninth aspect, the second stop number prediction means predicts that the vehicle will stop at an intersection with a road having a higher priority than the own vehicle on the planned traveling route.

According to the ninth aspect of the present invention, since it is predicted that the vehicle stops at an intersection with a higher priority road among the intersections in the planned driving route, it is possible to predict a stopping place more suitable for actual driving. .

Further, in the running pattern generating device according to the tenth aspect, the second stop number prediction means predicts that the vehicle will stop at a tollgate on the planned traveling route.

According to the tenth aspect of the present invention, if there is a toll booth on the scheduled traveling route, it is predicted that the vehicle will stop at the toll booth.

Further, the running pattern generating device according to the eleventh aspect further comprises running history storage means for storing a past running history, and the second stop number prediction means includes a past history stored in the running history storage means. The stop locations are selected in order from an intersection or a traffic signal in which the ratio of the number of stops to the number of runs is large based on the travel history.

According to the eleventh aspect of the present invention, the stop locations are selected in order from the intersection or the traffic signal in which the ratio of the number of stops to the number of passes using the past travel history is high, so that the actual operation is performed. It is possible to predict a suitable stopping place.

According to a twelfth aspect of the present invention, when the traffic condition on the scheduled route changes, the running pattern is generated again based on the changed information.

According to the twelfth aspect of the present invention, when the traffic condition on the scheduled traveling route changes, the traveling pattern prediction is performed again, so that the traveling pattern prediction corresponding to the change in the traffic information while arriving at the destination can be made. .

(3) In order to achieve the above object, the driving pattern generating device according to the thirteenth aspect has road information obtaining means for obtaining information on a scheduled driving route of the vehicle.
In a travel pattern generation device that generates a travel pattern of the own vehicle on a planned travel route using information acquired by the road information acquisition unit, the travel route is divided based on the information acquired by the road information acquisition unit. Route segmenting means, traveling speed predicting means for estimating the traveling speed of the own vehicle based on the road information in the section segmented by the route segmenting means, and acceleration / deceleration cycle during traveling in the section segmented by the route segmenting means Acceleration / deceleration cycle prediction means for predicting, and acceleration / deceleration amplitude prediction means for predicting acceleration / deceleration amplitude during traveling in a section sectioned by the route dividing means,
While traveling, using a periodic function based on the cycle obtained by the acceleration / deceleration cycle prediction means, the amplitude obtained by the acceleration / deceleration amplitude prediction means, and the center of the amplitude obtained by the traveling speed prediction means. And a running pattern generating means for generating the speed pattern waveform.

According to the thirteenth aspect of the present invention, since the traveling pattern is generated by a periodic function based on the acceleration / deceleration cycle, the amplitude, and the center of the amplitude during the traveling, the travel pattern including the acceleration / deceleration during the traveling is obtained. Can be accurately predicted.

The driving pattern generating device according to a fourteenth aspect of the present invention includes a signal interval calculating means for obtaining a signal interval by dividing the length of the section divided by the route dividing means by the number of signals existing in the section. The acceleration / deceleration cycle prediction means corrects the acceleration / deceleration cycle during traveling according to the signal interval obtained by the signal interval calculation means, and the acceleration / deceleration amplitude prediction means obtains the acceleration / deceleration amplitude by the signal interval calculation means. The acceleration / deceleration amplitude during traveling is corrected according to the signal interval obtained.

According to the fourteenth aspect of the present invention, the acceleration / deceleration cycle and the acceleration / deceleration amplitude are corrected according to the signal interval, so that a traveling pattern closer to the actual traveling can be generated.

The driving pattern generating device according to a fifteenth aspect further comprises road type detecting means for detecting a road type of each of the sections divided by the route dividing means, and wherein the acceleration / deceleration cycle predicting means comprises: The acceleration / deceleration cycle during traveling is corrected according to the road type obtained by the type detection unit, and the acceleration / deceleration amplitude prediction unit is configured to output the acceleration / deceleration amplitude during traveling according to the road type obtained by the road type detection unit. Is corrected.

According to the fifteenth aspect of the present invention, the acceleration / deceleration cycle and the acceleration / deceleration amplitude are corrected according to the type of road, so that a traveling pattern closer to the actual traveling can be generated.

Further, the driving pattern generating apparatus according to the present invention further comprises congestion degree detecting means for detecting the congestion degree of each of the sections divided by the route dividing means, and wherein the acceleration / deceleration cycle predicting means comprises: The acceleration / deceleration cycle during traveling is corrected according to the congestion degree obtained by the degree detection means, and the acceleration / deceleration amplitude prediction means is configured to output the acceleration / deceleration amplitude during traveling according to the congestion degree obtained by the congestion degree detection means. Is corrected.

According to the sixteenth aspect of the present invention, since the acceleration / deceleration cycle and the acceleration / deceleration amplitude are corrected according to the congestion degree, a traveling pattern closer to the actual traveling can be generated.

In a preferred embodiment, the driving pattern generating apparatus further comprises a signal interval calculating means for calculating a signal interval by dividing the length of the section divided by the route dividing means by the number of signals existing in the section. A road type detecting unit for detecting a road type of each section sectioned by the route dividing unit;
Running speed detecting means for detecting a running speed of the host vehicle, wherein the acceleration / deceleration cycle predicting means uses a running speed obtained by the running speed detecting means, and a signal interval obtained by the signal interval calculating means. And learning the acceleration / deceleration cycle excluding the start acceleration from the stop and the deceleration until the stop, in accordance with the road type obtained by the road type detection means, and correcting the predicted value, thereby obtaining the acceleration / deceleration amplitude prediction. Means for starting acceleration from a stop using the traveling speed obtained by the traveling speed detecting means and according to the signal interval obtained by the signal interval calculating means and the road type obtained by the road type detecting means. Then, the prediction value is corrected by learning the amplitude of the acceleration / deceleration excluding the deceleration up to the stop.

According to the seventeenth aspect of the present invention, the acceleration / deceleration cycle and the acceleration / deceleration amplitude during traveling are learned sequentially according to the signal interval and the road type during traveling, and the learned values are used when predicting the traveling pattern. Therefore, it is possible to generate a traveling pattern closer to the actual traveling.

The driving pattern generating device according to claim 18, further comprising a curve detecting means for detecting a curve of each section divided by the route dividing means, wherein the acceleration / deceleration cycle predicting means includes the curve detecting means. The acceleration / deceleration cycle during travel is corrected in the curve obtained by (1), and the acceleration / deceleration amplitude prediction means corrects the acceleration / deceleration amplitude during travel in the curve obtained by the curve detection means.

According to the eighteenth aspect of the invention, since the acceleration / deceleration cycle and the acceleration / deceleration amplitude are corrected according to the curve, a traveling pattern closer to the actual traveling can be generated.

(4) The running pattern generating device according to claim 19 calculates the energy consumption of the own vehicle in the running route to the destination based on the running pattern of the own vehicle generated by the running pattern generating means. And an energy consumption calculating means.

According to the nineteenth aspect of the present invention, the driving pattern is used in consideration of stopping and starting on the driving route to the destination, so that the driving force assist for regeneration of deceleration energy at the time of stopping and acceleration at the time of starting is used. Is taken into account and the exact energy consumption can be calculated.

According to a twentieth aspect of the present invention, there is provided a driving pattern generating apparatus, comprising: an energy source for the self-vehicle in a driving route to a destination based on a driving pattern of the host vehicle generated by the driving pattern generating part. And an energy balance schedule calculating means for calculating a storage and release schedule.

According to the twentieth aspect of the present invention, since a traveling pattern is used in consideration of stopping and starting on the traveling route to the destination, a driving force assist for regeneration of deceleration energy at the time of stopping and acceleration at the time of starting is used. Can be taken into account to determine the exact energy storage and release schedule.

[0044]

(1) According to the first to fourth aspects of the present invention, particularly in a vehicle such as a hybrid vehicle having energy storage means, an energy balance due to a stop and a start required for actual running is taken into consideration. Driving pattern, and more accurate energy storage and release scheduling.

In addition, according to the fifth aspect of the present invention, it is possible to predict the number of stops according to the characteristics of the road.

According to the sixth aspect of the present invention, the number of stops adapted to the actual traffic situation can be predicted, such as making the number of stops larger in the morning and evening rush hours and smaller in the middle of the night or early morning.

Further, according to the inventions of claims 7 to 11, it is possible to predict a stopping place more suitable for actual driving.

According to the twelfth aspect, it is possible to predict a traveling pattern corresponding to a change in traffic information while arriving at a destination.

(2) According to the thirteenth to eighteenth aspects, the running pattern is generated by a periodic function based on the acceleration / deceleration cycle, the amplitude, and the center of the amplitude during the running. It is possible to accurately predict a traveling pattern to a destination including the destination.

In addition, according to the present invention, the acceleration / deceleration cycle and the acceleration / deceleration amplitude are determined according to the signal interval, the road type, the degree of congestion, the curve, or using the learning function. Since the correction is made, a traveling pattern closer to the actual traveling can be generated.

(3) According to the nineteenth aspect of the present invention, a strict calculation of energy consumption can be performed by taking into account regeneration of deceleration energy at the time of stopping and driving force assist for acceleration at the time of starting. .

Further, according to the present invention, it is possible to determine a strict energy accumulation and release schedule in consideration of regeneration of deceleration energy at the time of stopping and driving force assist for acceleration at the time of starting. it can.

[0053]

Embodiments of the present invention will be described below with reference to the drawings. First Embodiment As shown in FIG. 1, a traveling pattern generation device 1 of the present embodiment includes a road information acquisition unit 11, a clock 12, a stop count estimation unit 13, a traveling speed estimation unit 14, a acceleration / deceleration estimation unit 1.
5 and a running pattern generating means 16.

The road information obtaining means 11 of this embodiment is constituted by a navigation device, and detects information on a route on which the vehicle is to travel to a destination (hereinafter also referred to as a scheduled travel route). The navigation device is a GPS antenna for measuring the position of the vehicle, a gyro compass, road map data, and a VICS (Vehicle Information & Communication System) for receiving traffic information such as traffic congestion.
tem) receiver, and the road map data records the curvature, width, type and intersection of the road, the position of the traffic light, and the like.

The clock 12 of the present embodiment outputs the current time, and is used for estimating the time at which the own vehicle passes the route to be traveled.

The number-of-stops prediction unit 13 predicts the number of stops using the information of the road type, the intersection, the position of the traffic light, and the time from the clock 12 of the planned traveling route obtained by the road information acquisition unit 11.

The traveling speed predicting means 14 predicts the traveling speed using the information on the road type and the curvature of the planned traveling route obtained by the road information acquiring means 11.

The acceleration / deceleration predicting means 15 predicts the acceleration / deceleration of the planned traveling road from data on the road type and the past traveling history of the planned traveling route obtained by the road information acquiring means 11.

The traveling pattern generating means 16 combines the prediction results of the number-of-stops prediction means 13, the traveling speed prediction means 14, and the acceleration / deceleration prediction means 15 to generate a traveling pattern on the planned traveling route to the destination, for example, a speed pattern. .

Next, the control contents of this embodiment will be described. FIG.
4 is a flowchart showing a process of the traveling pattern generation device 1 of the present example, which is called at a predetermined time interval from a main program of a navigation device or a control unit of an automatic transmission or an engine.

To explain this, first, in step S
In step 11, a route to the destination is set by the navigation device, and the road type, curvature, gradient, traffic light,
Information about the position of the intersection is read from the road map data.

Next, in step S12, step S1
The route to the destination set in 1 is divided into several sections with a point where the road type changes and a main intersection as a delimiter. For example, in the case of the route shown in FIG. 2, the route from the departure point to the intersection A which hits the two-lane road and the intersection A
It is divided into the intersection B between the lane lane roads, the intersection C that turns right from this intersection B to a narrow street, the intersection D that goes out of this intersection C to a one-lane road, and the section from this intersection D to the destination. I do. Then, the subsequent work of speed pattern prediction is performed sequentially from the section on the departure point side.

In step S13, a predicted value (n) of the number of stops is obtained from the average interval (X) between the intersections and the traffic signals using, for example, a monotonically decreasing function such as the following equation 1. Equation 1 can be obtained from experimental data as shown in the graph of FIG.

[0064]

N = {− 3.85 · ln (x) +23.6} / 1000 Equation 1

Here, n is the predicted number of stop times, x is the average signal interval, and l is the section extension [m].

Here, the route shown in FIG.
When the predicted number of stop times of each section divided by is calculated by the above equation 1, the following table 1 is obtained. In addition, here, the decimal point of the predicted stop count is rounded and used,
Depending on the time of the clock 12 shown in FIG. 1, for example, from 7:00 to 9:00 and from 17:00 to 20:00, the decimal part is rounded up, and
The time may be rounded up by 0.5 or more from 17:00 to 17:00, and may be rounded down from 20:00 to 7:00. The setting of the time zone here is appropriately changed according to the place and the traveling direction.

[0067]

[Table 1]

Next, a method of selecting a traffic light or an intersection that stops within one section will be described. First, for an intersection that goes straight ahead, a stop place is selected according to a priority order such as an intersection with a 12-lane road and an intersection with a 21-lane road. In addition, toll booth, right turn (in case of left-hand traffic), right / left turn to higher priority road (narrow street → 1 lane road, 1
In the case of a two-lane road (lane road), it is predicted that the vehicle will always stop regardless of the predicted number of stops.

Now, the selection of an intersection to be stopped will be described by taking the route shown in FIG. 2 as an example. According to Table 1, the number of predicted stops in the section from the departure point to the intersection A is 2.6. Predict to round up and stop three times.
Then, as this stop location, an intersection A that turns left from a one-lane road to a two-lane road, an intersection A that is an intersection with a two-lane road
A1 and A2, which is an intersection with a one-lane road, are selected.

Similarly, the predicted number of stops in the section from the intersection A to the intersection B is 1.1 from Table 1, and it is predicted that one stop will be performed by truncating the decimal part. However, in this section, there is a toll gate B2 that is predicted to be "always stopped" and an intersection B that makes a right turn, so the number of predicted stops is one, but it stops at two places, the toll gate B2 and the intersection B. Then predict.

In the case of a section where the vehicle has traveled in the past, a stop place is selected in order from an intersection or a traffic signal having a high stop probability (number of stops / number of passes) using the travel history at that time. May be predicted.

In step S14, the traveling speed is predicted using the information collected in step S11. As an example of a method for predicting the traveling speed (Vs [km / h]), a method using the type of road and the curvature of a curve as shown in the following equation 2 is considered. Here, Vcrv (r) is a function for the radius of curvature r [m] of the road as shown in FIG. 4, for example.

[0073]

Vs = Vcrv (r) + Ts Equation 2

However, Ts is -10 when the scheduled route is a narrow street, 0 when it is a one-lane road, 10 when it is a two-lane road, and 45 when it is a highway.

In step S15, the acceleration / deceleration is predicted using the information collected in step S11. Here, acceleration (Acc [m / s ² ]) and deceleration (D
ec [m / s ² ]), there is a method using a road type as shown in the following equations 3 and 4.

[0076]

## EQU3 ## Acc = 0.76 + Ta ... Equation 3

However, Ta is 0 when the scheduled traveling route is a narrow street or a one-lane road, and is -0 when the planned route is a two-lane road.
03, 0.15 for an introductory road of an expressway, and -0.03 for a main road of an expressway.

Dec = −1.11 + Td (Equation 4) where Td is 0 when the planned route is a narrow street or a one-lane road, −0.14 when the planned two-lane road is a road, and when the planned route is an introduction road of an expressway. Is 0.03, and 0.36 for the main line of the highway.

Table 2 below shows the results of predicting the traveling speed, acceleration and deceleration on the route shown in FIG. 2 using equations 2 to 4 in steps S14 and S15.

[0080]

[Table 2]

Next, in step S16, step S1
A predicted waveform of a traveling pattern is generated for each section by combining the number of stops, the traveling speed, and the acceleration / deceleration predicted in steps S3 to S15. FIG. 5 shows running patterns generated according to the prediction results of the number of stops and the running speed, acceleration, and deceleration on the route shown in FIG. 3 shown in Tables 1 and 2 above.

When such a running pattern is used for vehicle control such as determination of a battery charge / discharge schedule for a hybrid vehicle, it is more convenient to use the time axis as a reference. A method for converting the distance axis shown in FIG. 5 to the time axis shown in FIG. 6 will be described below.

As shown in FIG. 6, Sa [m] and Sd [m] represent the distance traveled during acceleration and deceleration, Ss [m] represents the distance traveled at constant speed, and the distance traveled from the start to the stop (between intersections). ) Is L [m], the following equation holds.

[0084]

L = Sa + Ss + Sb Equation 5

Here, Sa and Sd are the running speeds V
It can be expressed as follows using s, acceleration Acc, and deceleration Dec.

[0086]

[Number 5] ^{Sa = (Vs / 3.6) 2} / 2Acc ... Equation ^{6 Sd = (Vs / 3.6)} 2 / 2Dec ... formula 7

The time Ts [sec] for traveling at a constant speed can be expressed as follows.

[0088]

Ts = Ss / (Vs / 3.6) Equation 8

By substituting Equations (6) to (8) into Equation (5),
Ts is obtained as follows.

[0090]

Ts = L / (Vs / 3.6) − (Vs / 3.6) (1 / Acc−1 / Dec) / 2 Equation 9

In this manner, the acceleration time, the constant speed traveling time, and the deceleration time are determined from the predicted values of the traveling speed, the acceleration, and the deceleration, and the traveling pattern based on the time axis can be generated.

Finally, in step S17, it is determined whether or not the running pattern prediction for all sections to the destination has been completed.

As described above, according to the traveling pattern generation device 1 of the present embodiment, the traveling speed, acceleration, deceleration, and deceleration are obtained by using the information on the road type of the route to the destination, the curvature of the curve, the position of the intersection and the traffic light. , Predicts the number of outages,
In particular, by predicting the number of stops using the average interval between intersections and traffic lights, it is possible to generate a traveling pattern that takes into account road conditions.

In addition, the processing of the number after the decimal point of the number of predicted stop times is rounded off during the day according to the time zone, and the number of rounded-down decimal places is reduced to 4 or less during the morning and evening rush hours when traffic is heavy. By increasing the round-down number after the decimal point to 6 or more in the case of late night or early morning with a small amount, the number of stops adapted to traffic conditions can be predicted.

Further, at intersections where the vehicle turns right, intersections where the vehicle turns left or right on a road having a higher priority, or at a tollgate, it is predicted that the vehicle will always stop at that location. Therefore, the number of stops suitable for actual driving can be predicted.

Furthermore, since it is predicted that an intersection that goes straight ahead will stop at an intersection with a higher priority road, the number of stops suitable for actual driving can also be predicted at this point.

In a section where the vehicle has traveled in the past, the stop probability ｛(the number of stops) is determined using the past travel history.
Since the stop place is selected in order from the intersection or the traffic signal having the highest / (number of passes), it is possible to predict the number of stops suitable for the actual operation.

Second Embodiment Next, a second embodiment of the present invention will be described. The control content of this embodiment is basically the same as that shown in FIG. 7, except that the generation of a traveling pattern in a congested section is added in step S16. In addition, a process for re-estimating a traveling pattern for responding to a change in traffic conditions shown in FIG. 8 is added.

Hereinafter, the description of the control processing common to the first embodiment will be omitted, and the processing performed in step S16 will be described. In step S16, when the traffic congestion information is received on the route to the destination by the VICS receiver horizontal to the navigation device, the number of stops in the traffic congestion section is predicted as follows in the case of a general road. I do.

That is, FIG. 9 shows a model of a traveling pattern during a traffic jam. In this case, it is assumed that the vehicle repeatedly accelerates until reaching a certain speed, then immediately starts decelerating and stops. Assuming that the maximum value of the speed of the traveling pattern shown in FIG. 9 is a value obtained by multiplying the average traveling speed (Vj [km / h]) of the congested section transmitted from the VICS center by a certain value (for example, 2). The distance (lj [m]) that travels from the start to the stop of the round is as follows.

[0101]

## EQU8 ## lj = 2Vj ² (1 / Acc + 1 / Dec) Equation 10

Therefore, assuming that the length of the congested section is L [m], the number of stops in the congested section is L / lj [times]. Also, an average time interval Tj [se] from the stop to the next start
c] is as follows.

[0103]

Tj = (L / Vj) / (L / lj) = lj / Vj Equation 11

Based on the number of stops determined above and the average time interval Tj from the stop to the next start, a running pattern in a congested section as shown in FIG. 9 is generated.

In the case of congestion on an expressway,
Since it is considered that the vehicle travels at a low speed without stopping, the traveling pattern shown in FIG. 9 may be shifted to a constant speed (for example, 0.5 Vj) and used. At this time, lj and the number of stops (n [times]) shown in Expression 10 are as follows.

[0106]

Lj = (1.5 Vj) ² (1 / Acc + 1 / Dec) / 2 Expression 12 n = (L / 2) / lj

When the actual running pattern and the predicted running pattern for the first fixed time in the congested section are compared with each other and the error is large (for example, the cross-correlation coefficient is 0.5 or less), the actually measured running pattern is changed to the congested section. May be modified to repeat until the end of.

As in the control mode shown in FIG. 7, the control of this embodiment is called at regular time intervals from the main program of the navigation unit or the control unit of the automatic transmission or the engine. In step S21, the VICS receiver receives traffic information on the route to the destination. In step S22, the difference between the traffic information received in step S21 and the information received before is checked in order to detect a new occurrence or elimination of traffic congestion.

In step S23, the prediction of the traveling pattern shown in FIG. 7 is performed again in order to consider the change in the traffic situation when the traffic information received in S21 is changed.

Therefore, when the traffic information on the route to the destination is received by the VICS receiver and the traffic situation changes, the prediction of the traveling pattern is performed again, so that the traffic information changes while arriving at the destination. The running pattern corresponding to can be predicted.

Third Embodiment FIG. 10 is a block diagram showing the configuration of a running pattern generation device 10 according to a third embodiment of the present invention. To explain this, the positioning sensor 101 is composed of a GPS antenna for receiving radio waves from satellites and a gyro compass for measuring the traveling direction of the vehicle.

The traffic information detecting means 102 includes a receiver for information from infrastructure such as FM multiplex broadcasting, radio beacons, and optical beacons, and collects traffic information such as road construction and congestion in real time.

The speed detecting means 103 is constituted by a magnetic sensor or an optical sensor, and detects the running speed of the vehicle or the rotation speed of the engine from the rotation speeds of the wheels and the drive shaft.

The vehicle position detecting means 104 compares the latitude, longitude and road shape obtained from the GPS antenna with the road shape on the surrounding map data and the traveling locus of the vehicle from the gyro compass and the speed detecting means. Identify the location of your vehicle.

In the destination input means 105, the driver scrolls the display screen of the map to set the destination on the map data.

The route searching means 106 searches the map data for a route from the vehicle position specified by the vehicle position detecting means 104 to the destination set by the destination input means 105.
The route search condition may be the shortest distance or preferential use of an expressway.

The route dividing means 107 is provided with the route searching means 10
The route to the destination searched in step 6 is divided into several sections according to the type of road, the intersection where the vehicle turns left and right, and the toll booth.

The road information detecting means 108 detects signal intervals, road types, and traffic information from map data for each section to the destination divided by the route dividing means 107.

The acceleration / deceleration cycle prediction means 109 and the acceleration / deceleration amplitude prediction means 110 predict the acceleration / deceleration cycle and amplitude during traveling according to the signal interval detected by the road information detection means 108, the road type, and the traffic information.

The stop position predicting means 111 predicts the stop position from the signal detected by the road information detecting means 108, the intersection, and the position of the tollgate.

The traveling speed / acceleration / deceleration predicting means 112 predicts the traveling speed, the acceleration at the time of starting, and the deceleration at the time of stopping from the road type and the curvature detected by the road information detecting means 108.

The running pattern generating means 113 includes acceleration / deceleration cycle prediction means 109, acceleration / deceleration amplitude prediction means 110, stop position prediction means 111, traveling speed / acceleration / deceleration prediction means 11
The traveling pattern to the destination is predicted using the prediction result of (2).

The function of each block is shown in FIG.
As shown in FIG. 0, components other than the speed detection means 103 may be integrated in the navigation device, and the acceleration / deceleration cycle prediction means 109, the acceleration / deceleration amplitude prediction means 110, the running pattern generation means 113, etc. may be independent arithmetic units. Good.

FIG. 11 shows an example of hardware for realizing such a configuration. In this example, a positioning sensor composed of a GPS antenna, a gyro compass, and the like, and a receiver for receiving traffic information from infrastructure such as VICS and map data recording road gradients, road types, intersection positions, and the like. The configured navigation device, accelerator opening, throttle opening, vehicle speed, engine speed, motor speed, SOC (battery charge rate, State of Cha
rge), and a hybrid control unit that controls the driving force of the engine and the motor and the SOC of the battery according to the input signals from the sensors and the brake switch.
Here, a recordable DVD drive, flash memory, or the like is used in the map data recording device according to the present invention in order to record and update the travel history.

Next, the control procedure will be described. FIG. 12 is a flowchart showing a basic control procedure of this example, and shows an outline of a procedure for predicting a traveling pattern using road information on a route to a destination.

First, at step 11, the destination input means 1
At 05, the driver inputs a destination. It should be noted that the navigation device records the history of the traveling route, and if the vehicle is traveling on the same route in the same time zone as the past history, even if the driver does not set the destination, such as commuting road, etc. The destination may be automatically set by predicting that the vehicle travels on a route that is often used.

In step 12, the route searching means 106 searches for a route to the destination set in step 11. The conditions for the route search may be such that the driver can select, for example, use of a toll road or avoidance of a congested section.

In step 13, the road information and traffic information of the route searched in step 12 by the route dividing means 107 are extracted from the traffic information detecting means 108 and the map data.
Road type, intersection to turn left and right, tollgate, change point of slope,
The road is divided into several sections depending on the traffic congestion.

In the following steps 14 to 18,
The travel patterns in each section divided in step 13 are generated in order from the current location to the destination.

First, at step 14, the stop position (S1 to Sm) is predicted from the position information of the intersection or the tollgate by the stop position predicting means 111. For the prediction of the stop position, a method of predicting the number of stops at an intersection that goes straight ahead from the average interval between the intersections can be adopted as in the first embodiment described above.

That is, the predicted value (n) of the number of stops is obtained from the average distance (X) between the intersections and traffic signals by using the monotone decreasing function as in the above equation (1). Regarding a method of selecting a traffic light or an intersection that stops in one section, first, for an intersection that goes straight ahead, an intersection with a 12-lane road,
A stop place is selected according to a priority such as an intersection with a one-lane road. In the case of a tollgate, right turn (for left-hand traffic), or right / left turn to a higher priority road (narrow street → 1 lane road, 1 lane road → 2 lane road), regardless of the predicted number of stops Predict to stop.

The selection of the intersection to be stopped will be described by taking the route shown in FIG. 2 as an example. According to Table 1, the predicted number of stops from the departure point to the intersection A is 2.6, and Is predicted to stop three times.
Then, as this stop location, an intersection A that turns left from a one-lane road to a two-lane road, an intersection A that is an intersection with a two-lane road
A1 and A2, which is an intersection with a one-lane road, are selected.

Similarly, the number of prediction stops in the section from the intersection A to the intersection B is 1.1 from Table 1, and it is predicted that the stop will be performed once by truncating the decimal part. However, in this section, there is a toll gate B2 that is predicted to be "always stopped" and an intersection B that makes a right turn, so the number of predicted stops is one, but it stops at two places, the toll gate B2 and the intersection B. Then predict.

In the case of a section where the vehicle has traveled in the past, using the traveling history at that time, a stop place is selected in order from an intersection or a traffic signal having a high stop probability (stop count / pass count). May be predicted.

Returning to FIG. 12, in step 15, the traveling speed / acceleration / deceleration prediction means 112 uses a table in which the relationship between the road type and the traveling speed, acceleration, and deceleration as shown in FIG. The traveling speed (Vm [km / h]) and acceleration (Am
[M / s ² ]) and deceleration (Dm [m / s ² ]). The values in the table shown in FIG. 13 may be recorded by averaging the traveling speed, acceleration, and deceleration in traveling up to the time of execution of this calculation for each road type. acceleration,
Deceleration, the distinction between running during when acceleration is above a certain value (e.g., 0.5 [m ^{/ s} 2]) is accelerated, a constant value (e.g., -0.5 [m ^{/ s} 2]) when the following During deceleration, at other times, the vehicle is running.

In the next step 16, the traveling pattern generation means 113 reads the acceleration / deceleration cycle (Tm [sec]) recorded in the acceleration / deceleration cycle prediction means 109 during traveling.

In step 17, the traveling pattern generating means 113 outputs the speed amplitude (Wm [km / km) in the acceleration / deceleration during traveling recorded in the acceleration / deceleration amplitude prediction means 110.
h]). The recorded values of the acceleration / deceleration cycle Tm and the speed amplitude Wm during traveling are obtained from actual traveling data of a route including a plurality of road types such as an expressway and a general road, and the acceleration / deceleration cycle and speed amplitude during traveling. Are extracted and their average values are determined and set.

In step 18, a running pattern is generated based on the stop position, running speed, acceleration, deceleration, acceleration / deceleration cycle during running, and amplitude of speed predicted in steps 14 to 17. As shown in FIG. 14, the traveling pattern may set the predicted speed V (n) at regular time intervals, or may calculate the predicted speed V (n) for each node representing the coordinates of the road recorded in the map data or for each complementary point. (N) may be determined. In the example of FIG. 14, acceleration is performed at the acceleration Am from the stop position S1 until the traveling speed Vm is reached.
A running pattern is generated at regular time intervals using a periodic function of m, amplitude center Vm, and amplitude Wm. When a sine wave is used for the periodic function as shown in FIG. 14, a running pattern in which the acceleration changes continuously can be generated. Also, using a triangular wave,
The calculation load at the time of generating the running pattern can be reduced.

The deceleration is performed when the remaining distance to the next stop position S2 becomes smaller than the distance required to stop at the acceleration Dm from the current speed V (i), that is, from the time when the following equation is satisfied. The speed is reduced by the acceleration Dm until the speed becomes 0 [km / h].

[0140]

L−∫V (n) dt ≦ V (i) ² / (2 | Dm |) Equation 13

Here, L [m] is from the stop position S1 to S2.
∫V (n) [m] is the running distance from S1.

In step 19, if the target section for which the running pattern is to be predicted is the last section on the route to the destination, the present control is terminated. If not, the procedure returns to step 14 to predict the running pattern of the next section.

The above is the basic control procedure of this example.
Of these, the control contents of step 16 and step 17 can be changed as follows.

First, in step 16, acceleration / deceleration period predicting means 109 divides the length of the section divided in step 13 by the number of signals in the section, and calculates the acceleration during traveling according to the signal interval (Sim [m]). The deceleration cycle Tm is corrected. Also,
In step 17, the acceleration / deceleration amplitude predicting means 110 corrects the speed amplitude Wm in the acceleration / deceleration during running according to the signal interval Sim, as in step 16. Such a correction value of the cycle Tm and the amplitude Wm can be obtained, for example, from a relational expression as shown in FIG. In addition,
The relational expression of FIG. 15 indicates that, for example, the shorter the signal interval, the shorter the acceleration / deceleration cycle during traveling and the smaller the amplitude of the speed.
It is desirable to reflect the tendency obtained as a result of analyzing actual driving data.

As another modified example, in step 16, the acceleration / deceleration cycle T during running is determined by the acceleration / deceleration cycle prediction means 109 in accordance with the road type for each section divided in step 13.
Correct m. In step 17, the acceleration / deceleration amplitude predicting means 110 corrects the speed amplitude Wm in the acceleration / deceleration during traveling according to the road type, as in step 16. The correction values of the acceleration and deceleration cycle and the amplitude of the speed during the running are based on a highway, a general road (one lane), and a general road (2 lanes).
It is desirable to extract the acceleration and deceleration cycle and the amplitude of the speed during traveling from the actual traveling data for each road type such as lane, and determine and set the average value thereof.

As still another modification, in step 16, the congestion degree information (average) for each section divided in step 13 obtained from the infrastructure such as VICS by the acceleration / deceleration cycle prediction means 109 and the traffic information detection means 102 is obtained. The acceleration / deceleration cycle Tm during traveling is corrected according to the traveling speed (TDm [km / h]). In step 17, the acceleration / deceleration amplitude predicting means 110 corrects the amplitude Wm of the speed during acceleration / deceleration during traveling according to the congestion degree (average traveling speed: TDm) as in step 16. Such a correction value of the cycle Tm and the amplitude Wm can be obtained, for example, from a relational expression as shown in FIG. Note that FIG.
It is desirable that the relational expression reflects the tendency obtained as a result of analyzing actual traveling data, such as the higher the congestion degree, the shorter the acceleration / deceleration cycle during traveling and the greater the amplitude of the speed.

As described above, by appropriately correcting the acceleration / deceleration cycle and amplitude in accordance with the signal interval, the road type or the degree of congestion, the obtained traveling pattern can be made closer to the actual traveling.

Fourth Embodiment Next, a fourth embodiment of the present invention will be described. This fourth
The control content of this embodiment is basically the same as that of the third embodiment described above, except that the running pattern during running is recorded for each section divided in step 13 in FIG. By calculating the amplitude and learning in accordance with the road type and the signal interval of the section, the acceleration and deceleration cycle during running and the predicted value of the amplitude are sequentially corrected.

FIG. 17 is a flowchart showing the control procedure of this embodiment. First, at step 21, the running speed V (i)
Record The recording of the traveling speed may be performed at a fixed time interval as shown in FIG. 18, for example, or may be performed for each node representing the coordinates of the road recorded in the map data or for each complementary point.

In step 22, if the running position is at the end of the section, the process proceeds to step 23, where the calculation of the acceleration / deceleration cycle and amplitude during the running is started from the running pattern of the section that has been running so far. For example, the process returns to step 21 to continue recording the traveling speed.

At step 23, a difference V (i) -V (i-1) of the running speed is calculated for the running pattern of the section which has been running so far, which was recorded at step 21.

In step 24, as shown in FIG.
The running pattern of the section that has been running up to now
The code | symbol of the difference calculated in 3 is divided | segmented into the same area continuously, ie, the acceleration or deceleration continuous area.

In step 25, in the acceleration or deceleration continuation section divided in step 24, the speed is set to a constant value Vs
(For example, 15 [km / h]) or less are removed to exclude the starting acceleration from the stop and the deceleration to the stop. In the example of FIG. 18, the section from V (1) to V (2) and the section from V (8) to V (10) are excluded.

In step 26, the cycle of a continuous portion other than the portion removed in step 25 is calculated. The calculation of the cycle includes a method of obtaining a time for repeating “acceleration → deceleration” or “deceleration → acceleration” and setting the cycle as Tm. If two or more cycles of acceleration / deceleration are present in the section, the average value is taken. In the example of FIG. 18, T (4) −
The average of T (2) and T (8) -T (4) is defined as the acceleration / deceleration cycle during traveling. Further, a method of averaging a value obtained by doubling the length of each acceleration or deceleration continuation section to obtain the cycle Tm can also be adopted. In the example of FIG. 10, the following equation is obtained.

[0155]

Tm = 2 × ｛(T (3) −T (2)) + (T
(4) −T (3)) + (T (6) −T (4)) + (T
(8) -T (6))｝ / 4

In step 27, the absolute value of the speed change in each acceleration or deceleration continuation section other than the part removed in step 25 is obtained, and the average value is set as the amplitude Vm. FIG.
In the example, the amplitude is represented by the following equation.

[0157]

Wm = (| V (2) −V (3) | + | V
(3) -V (4) | + | V (4) -V (6) | + | V
(6) -V (8) |) / 4

In step 28, the predicted value so far is corrected using the cycle Tm and the amplitude Wm obtained in steps 26 and 27. This makes it possible to predict the acceleration and deceleration cycle and amplitude during traveling according to the driving tendency of the driver. As the correction, for example, as described below, a method using a weighted average of the past predicted values TM (n) and WM (n) (n is the number of corrections so far for each road type) can be given.

[0159]

(14) TM (n + 1) = (TM (n) + Tm) / 2 WM (n + 1) = (WM (n) + Wm) / 2

Alternatively, there is a method of recording the number of corrections n so far for each road type, and obtaining a weighted average using the number of corrections n as described below.

[0161]

[Expression 15] TM (n + 1) = (n × TM (n) + Tm) / (n + 1) WM (n + 1) = (m × WM (n) + Wm) / (n + 1)

Alternatively, there is also a method of recording the predicted values up to a certain number of times in the past and using an average value thereof as described below.

[0163]

[Expression 16] TM (n + 1) = {TM (n-p) + ... + Tm (n-1) + Tm (n)} / (p + 1) WM (n + 1) = {WM (n-p) + ... + Wm (n -1) + Wm (n)｝ / (p + 1)

The acceleration / deceleration cycle and the predicted value of the amplitude during traveling are recorded for each road type. In the example of FIG. 18, the predicted value for a general road (two lanes) is corrected.

In step 29, if the target section for correcting the predicted value of the acceleration / deceleration cycle and the amplitude during traveling is the last section on the path to the target, the control is terminated. Otherwise, the control returns to step 14 to return to the next step. Perform section correction.

Fifth Embodiment FIGS. 19 to 21 show still another embodiment of the present invention. The control content of this fifth embodiment is basically the same as that of the above-described third embodiment. However, if there is a curve in the section where the traveling pattern is predicted, the amplitude of acceleration / deceleration during traveling is corrected to zero. It should be noted that, in addition to this, a method of correcting the acceleration and deceleration cycle when traveling in a curve section to be long and small in amplitude may be adopted. This control is a subroutine called from step 18 in FIG.

First, at step 211, the curvature r (i) at the point I in the section where the traveling pattern is predicted is set to the set value α.
If it is equal to or less than (for example, 1/50), it is regarded as a curve section and the process proceeds to step 212. If not, the process proceeds to step 215, and as shown in FIG.
Acceleration / deceleration is added to the traveling pattern during traveling in the same manner as in the process of FIG.

In step 212, the curve passing speed Vc is obtained from the curvature r (i). As the relational expression between the curvature and the curve passing speed, for example, a monotone decreasing function as shown in FIG. 21 can be used.

In step 213, as shown in FIG. 20, the speed V (i) at the point I in the section where the traveling pattern is predicted is set to Vc obtained in step 212. By doing so, a traveling pattern is generated such that the vehicle travels at a constant speed Vc from after entering the curve to before exiting as shown in FIG.

In step 214, if the running position is at the end of the section, the present control is terminated.
Returning to step 11, generation of the running pattern is continued.

As described above, in the traveling pattern generation devices of the third to fifth embodiments, the traveling pattern is generated by the periodic function based on the acceleration / deceleration period, the amplitude, and the center of the amplitude during traveling. The traveling pattern to the destination including the acceleration and deceleration of the vehicle can be accurately predicted. As a result, in the conventional traveling pattern generation device, as shown in FIG. 22A, the predicted pattern has poor matching with the actual traveling pattern and lacks the reliability of predicting the fuel consumption. In the pattern generation device, since the predicted pattern and the actual traveling pattern match well as shown in FIG. 4B, the reliability of prediction of fuel consumption and the like is significantly improved.

Other Embodiments Examples of the use of the traveling pattern on the route to the destination determined in the embodiment of the present invention include an HEV (hybrid vehicle) shown in FIG. 23 and an FCV (fuel cell electric vehicle) shown in FIG. The determination of the charge / discharge schedule of the battery provided in the vehicle).

Hereinafter, an example of a method of determining an energy charging / discharging schedule on a route to a destination using the traveling pattern predicted by the above procedure will be described.

The outline of the method for determining the charging / discharging schedule of this example is executed according to the following processing as shown in FIG.

First, the output required for traveling on the route to the destination is calculated using the predicted traveling pattern.
Then, the output required for traveling becomes negative, that is, the section is divided into several scheduling sections with a section where energy can be regenerated as a boundary.

Further, the value of the battery charge SOC at the end of the scheduling section is adjusted so that the regenerable energy in the next scheduling section can be charged to the maximum. For that purpose, motor driving in the corresponding scheduling section is considered.

When the value P (SOCmax-SOCp) (w) obtained by converting the SOCmax-SOCp into an output is smaller than the regenerable energy C (v) in the next scheduling section, the section in which the operating efficiency of the engine is low. From the motor. (However, SOCmax is the upper limit of the battery charge, SO
Cp is the battery charge at the beginning of the scheduling section. Further, when the value P (SOCmax-SOCp) (w) obtained by converting the SOCmax-SOCp into an output is larger than the regenerable energy C (w) in the next scheduling section, the case where the vehicle runs only with the engine and the case where the motor runs are used. The charge / discharge schedule is determined so as to perform the smaller one by comparing the fuel consumption when the power is generated in advance.

Based on the above basic idea, FIG.
The flow of charge / discharge schedule determination will be described with reference to the flowchart of FIG.

First, in step Sa1, it is searched whether or not there is a section on the route to the destination where energy can be continuously regenerated for a certain period of time (for example, one minute), and if so, the flow proceeds to step Sa2. Step Sa
Jump to 6. For example, in the route shown in FIG. 25, this section corresponds to a downhill on a general road due to deceleration before an exit tollgate on a highway.

In step Sa2, the route to the destination is divided into the scheduling sections by the sections in which the energy can be continuously recovered searched in step Sa1. FIG.
In the example of the above, the vehicle is divided into two scheduling sections at the boundary of the deceleration just before the exit of the expressway. The processing after step Sa3 is performed in order from the scheduling section on the departure place side.

In step Sa3, if the battery charge amount SOCp at the beginning of the scheduling section is near the lower limit value SOCmin, the process jumps to step Sa6; otherwise, the process proceeds to step Sa4.

In step Sa4, when the battery charge amount SOCp at the beginning of the scheduling section exceeds the upper limit value SOCmax during the energy regeneration, that is, when the SOC
The value P (SOCmax) obtained by converting max-SOCp into an output
If (SOCp) (v) is smaller than the regenerable energy C (v), the process proceeds to step Sa5; otherwise, the process jumps to step Sa6. In the example of FIG. 25, if the battery charge amount is the value SOCp at the beginning of the scheduling section 1, all the regenerable energy C (w) in the scheduling section 2 can be charged, and the process proceeds to step Sa6.

In step Sa5, a charging / discharging schedule is determined so that the battery is discharged by running or assisting the motor in the section where the engine operating point is farthest from the best efficiency line. In the example of FIG. 25, this corresponds to a congested section in an urban area.

In step Sa6, if the operating point of the engine is far from the best efficiency line, that is, if there is a section where the operating efficiency of the engine is low, the flow proceeds to step Sa7, and if not, the flow jumps to step Sa8. In the example of FIG. 25, this corresponds to a congested section in an urban area.

In step Sa7, the fuel is supplied when the engine travels only in the section where the operating efficiency of the engine detected in step Sa6 is low, or when the value of the battery charge SOC can be traveled by the motor, the fuel is deducted from the value of the battery. The amount of consumption B is compared with the amount of fuel consumption A in the case where power is generated in advance in order to charge the battery with energy for motor running in the section. When the fuel consumption amount A is smaller than B, power is generated in advance. In the example of FIG. 25, the fuel consumption B when the motor-driven energy is generated in the traffic congestion in the urban area on the previous general road is smaller than the fuel consumption A when the traffic congestion in the urban area in the scheduling section 1 is driven only by the engine. Because there are fewer,
The charge / discharge schedule is determined so that the battery is consumed by running the motor in a traffic jam in an urban area. The amount of power generation is set so that energy can be regenerated when stopped at the traffic light.

In step Sa8, if the scheduling section to be processed next is the last section, the flow advances to step Sa9; otherwise, the flow returns to step Sa3.

In step Sa9, a charge / discharge schedule is determined so that the battery is consumed by running or assisting the motor in a section where the engine operating point is farthest from the best efficiency line. In the example of FIG. 25, since there is only an uphill on a general road after energy regeneration in the final scheduling section, the battery is consumed by motor assist in this section.

The above is an example of a method for determining a battery charge / discharge schedule.

As described above, by using the prediction result of the traveling pattern in consideration of the stop / start on the route to the destination, the energy consumption on the route to the destination can be strictly calculated.

Further, by using the prediction result of the traveling pattern in consideration of the stop / start on the route to the destination, it is possible to strictly determine the energy accumulation and release schedule on the route to the destination.

The travel patterns obtained by the travel pattern generation devices according to the third to fifth embodiments of the present invention can also be applied to the above-described method of determining the energy charge / discharge schedule. That is, the operation efficiency of the engine is obtained from the running load, and the battery is charged in the section where the operation efficiency is high, and the battery charge / discharge schedule is generated such that the motor runs in the section where the operation efficiency is low.

The outline of the battery charge / discharge scheduling of the HEV will be described. First, as shown in FIG. 27, the method described in the flowchart of FIG.
Generate a traveling speed pattern to the destination. Next, using the road gradient and travel speed pattern of the route to the destination,
The running load pattern to the destination is calculated by combining the slope resistance, running resistance, and acceleration resistance. Then, the driving force distribution of the engine / motor is determined so as to reduce the fuel consumption while satisfying the traveling load pattern to the destination and the target SOC (battery charge rate) at the destination. Finally, the charge / discharge schedule is determined by determining that the SOC pattern from the driving force distribution to the destination falls within the range between the upper limit value and the lower limit value. In the example of FIG. 27, when the vehicle is traveling at a low speed in an urban area, the traveling load is small and the operation efficiency of the engine is low. Therefore, the motor traveling is prioritized, and a signal having a large area where the traveling load is negative, a deceleration in front of an intersection, and energy in a downhill section are obtained. And the charging / discharging schedule is determined so that the engine is assisted by the motor using the energy charged in the regeneration on the uphill after the downhill. By using the running pattern generated in consideration of acceleration and deceleration during running, the amount of regeneration due to deceleration during running and the torque required for acceleration can be accurately predicted. It can be performed well and fuel efficiency is improved. In addition, this application example can be applied not only to the HEV but also to an EV (electric vehicle) or an FCV. In this case, a signal, a deceleration in front of an intersection, or a section before energy can be regenerated in a downhill section or the like. Then, the battery is discharged, and the battery is charged just before a section requiring a large motor torque such as climbing a hill.

The running pattern generated by the running pattern generating apparatus of the present invention can be used for predicting the total amount of fuel consumed up to the destination by using the engine operation efficiency map and the fuel consumption rate map.

FIG. 28 is a flowchart showing a procedure for estimating the total amount of fuel consumed up to the destination.

First, in step 311, the running load P (i) [w] is obtained from each sample value V (i) of the speed pattern. The calculation of the traveling load uses the following relational expression.

[0196]

P (i) = １７Rr + Ra (i) + Rc (i)
+ Rg (i)｝ × V (i) where Rr = μMg, Ra = acc (i) M, Rc =
0.5ρCdAV (i) ² Rg = Mgsin {atan (Gr (i) / 100)}

Here, M (kg) is the vehicle weight, μ is the rolling resistance coefficient, ρ (kg / m ³ ) is the air density, Cd is the air resistance coefficient, A (m ² ) is the front shadow projection area, and g ( m / s ² ) is the gravitational acceleration, acc (m / s ² ) is the acceleration of the vehicle, Gr
(I) (%) is a calculated value of the road gradient.

In step 312, as shown in FIG. 29, the operating point associated with the work of the traveling load P (i) obtained in step 311 in the hybrid control unit is read out, and this is read from the fuel consumption rate map of the engine. The fuel consumption rate f (i) [cc / sec] at this time is obtained.

At step 313, the sample value V (i)
Is the last sample of the traveling pattern on the route to the destination, it is determined that the calculation of the fuel consumption rate for each sample value of the speed has been completed to the destination, and the process proceeds to step 314; otherwise, the process returns to step 311. .

In step 314, the total fuel consumption F [cc] is obtained by integrating the fuel consumption rate f (i) obtained in step 312 from the starting point to the destination as described below.

[0201]

F = ∫V (i) dt

As an application example of the fuel consumption prediction up to the destination, as shown in FIG. 30, when the destination is set by the navigation device, the fuel consumption calculated for a plurality of route candidates per unit amount is determined. A system that presents the fuel cost by multiplying the fuel cost is conceivable. This control can be applied not only to the HEV but also to the prediction of the fuel consumption of gasoline and diesel vehicles.

Although the embodiments of the present invention have been described above, the present invention is used not only for determining the charging / discharging schedule of the battery of a hybrid vehicle or FCV, but also for storing the heat and heat of an air conditioner equipped with a heat accumulator. The present invention may be applied to scheduling, or to more accurate calculation of fuel consumption to a destination in consideration of an energy balance caused by stopping and starting. Further, the present invention is not limited to the above embodiments, and may be implemented by combining any of them.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a first embodiment of a traveling pattern generation device according to the present invention.

FIG. 2 is a diagram illustrating an example of a scheduled traveling route.

FIG. 3 is a graph showing an example of the number of stops with respect to an average signal interval.

FIG. 4 is a graph showing an example of a traveling speed with respect to a radius of curvature of a road.

FIG. 5 is a pattern diagram showing an example of a traveling pattern generated by the traveling pattern generation device of the present invention.

6 is a graph obtained by converting the running pattern shown in FIG. 5 into a running pattern with respect to time.

FIG. 7 is a flowchart showing a control procedure of the traveling pattern generation device of the present invention.

FIG. 8 is a flowchart illustrating a control procedure according to a second embodiment of the present invention.

9 is a pattern diagram showing a traveling pattern of the embodiment shown in FIG.

FIG. 10 is a block diagram showing third to fifth embodiments of the traveling pattern generation device of the present invention.

FIG. 11 is a block diagram further embodying the embodiment shown in FIG. 10;

FIG. 12 is a flowchart illustrating a control procedure according to a third embodiment of the present invention.

FIG. 13 is a control map showing a relationship between a road type, a traveling speed, and acceleration / deceleration.

FIG. 14 is a graph showing a traveling pattern generated by the traveling pattern generation device shown in FIG.

FIG. 15 is a graph showing a relationship between a signal interval, an acceleration / deceleration cycle, and an amplitude.

FIG. 16 is a graph showing the relationship between the congestion degree and the acceleration / deceleration cycle and amplitude.

FIG. 17 is a flowchart illustrating a control procedure according to a fourth embodiment of the present invention.

FIG. 18 is an example of a traveling pattern for describing a control procedure according to a fourth embodiment.

FIG. 19 is a flowchart illustrating a control procedure according to a fifth embodiment of the present invention.

FIG. 20 is an example of a traveling pattern for describing a control procedure according to a fifth embodiment.

FIG. 21 is a graph showing a relationship between a curve passing speed and a curvature.

FIG. 22 is a diagram showing a traveling pattern obtained by the present invention and a conventional traveling pattern.

FIG. 23 is a block diagram illustrating an example of a hybrid vehicle to which the traveling pattern generation device according to the present invention is applied.

FIG. 24 is a block diagram showing an example of a fuel cell electric vehicle to which the traveling pattern generation device of the present invention is applied.

FIG. 25 is a pattern diagram showing an example in which a traveling pattern generated according to the present invention is applied to a hybrid vehicle.

26 is a flowchart showing a control procedure of the embodiment shown in FIG.

FIG. 27 is a pattern diagram showing another example in which the traveling pattern generated according to the present invention is applied to a hybrid vehicle.

FIG. 28 is a flowchart showing a procedure for obtaining a fuel consumption using the traveling pattern obtained by the present invention.

FIG. 29 is a graph showing the relationship between the engine speed and the engine output torque.

30 is a diagram showing an example in which the embodiment shown in FIG. 28 is applied to a navigation device.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 ... Running pattern generation apparatus 11 ... Road information acquisition means 13 ... Stop number prediction means 14 ... Travel speed prediction means 15 ... Acceleration / deceleration prediction means 16 ... Travel pattern generation means

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification code FI Theme coat ゛ (Reference) // B60K 1/04 G08G 1/0969 G08G 1/0969 B60K 9/00 ZHVC

Claims (20)

[Claims] 1. A traveling system comprising: road information acquisition means for acquiring information on a planned traveling route of a host vehicle; and generating a traveling pattern of the host vehicle on the planned traveling route using the information acquired by the road information acquiring means. The travel pattern generation device, further comprising: a stop count prediction unit that predicts the stop count of the host vehicle on the planned traveling route using the information acquired by the road information acquisition unit. 2. The traveling pattern according to claim 1, further comprising traveling speed prediction means for predicting the traveling speed of the host vehicle on the scheduled traveling route using the information acquired by said road information acquisition means. Generator. 3. The vehicle according to claim 1, further comprising acceleration / deceleration prediction means for predicting the acceleration / deceleration of the own vehicle on the scheduled traveling route using the information acquired by said road information acquisition means. A running pattern generator. 4. The running pattern of the host vehicle is determined based on the number of stops predicted by the number-of-stops prediction means, the traveling speed predicted by the traveling-speed prediction means, and the acceleration / deceleration predicted by the acceleration / deceleration prediction means. The traveling pattern generation device according to claim 3, further comprising a traveling pattern generation unit that generates the traveling pattern. 5. A stop number predicting means for detecting a position of an intersection and a traffic light on a planned traveling route of the own vehicle acquired by the road information acquiring means; A second stop number estimating means for dividing the route into several sections, obtaining an average interval between intersections and traffic signals existing in each section, and estimating the number of stops of the vehicle in each section based on the average interval; The travel pattern generation device according to claim 1, comprising: 6. The running pattern generating apparatus according to claim 5, wherein said second stop number predicting means changes the stop number according to a running time zone. 7. The travel pattern generation device according to claim 5, wherein the second stop number prediction means predicts that the vehicle will stop at an intersection where the own vehicle turns in a direction crossing an oncoming lane on the planned travel route. 8. The second number-of-stops estimating means predicts that the vehicle will stop at an intersection that turns right or left on a road with a higher priority than the own vehicle or crosses a road with a higher priority than the own vehicle on the planned traveling route. Item 6. A traveling pattern generation device according to item 5. 9. The travel pattern generation device according to claim 5, wherein the second stop number prediction means predicts that the vehicle will stop at an intersection with a road having a higher priority than the own vehicle on the planned travel route. 10. The travel pattern generation device according to claim 5, wherein the second stop number prediction means predicts that the vehicle will stop at a tollgate on the scheduled travel route. 11. A travel history storage means for storing a past travel history, wherein the second stop count prediction means is configured to determine a stop count for a travel count based on a past travel history stored in the travel history storage means. From the intersection or traffic light with the largest percentage of
The travel pattern generation device according to claim 5, wherein a stop location is selected. 12. The travel pattern generating apparatus according to claim 1, wherein when the traffic condition on the scheduled travel route changes, the travel pattern is generated again based on the changed information. 13. A traveling system having road information acquisition means for acquiring information on a planned traveling route of the own vehicle, and generating a traveling pattern of the own vehicle on the planned traveling route using the information acquired by the road information acquiring means. In the pattern generation device, a route dividing unit that divides a planned traveling route based on the information acquired by the road information acquiring unit; and a traveling speed of the own vehicle based on road information in a section divided by the route dividing unit. Traveling speed prediction means for predicting; acceleration / deceleration cycle prediction means for predicting an acceleration / deceleration cycle during traveling in a section divided by the route division means; acceleration / deceleration amplitude during traveling in a section divided by the path division means Acceleration / deceleration amplitude prediction means for predicting the acceleration / deceleration amplitude, a cycle obtained by the acceleration / deceleration cycle prediction means, and a vibration obtained by the acceleration / deceleration amplitude prediction means. And a traveling pattern generating means for generating a velocity pattern waveform during traveling by using a periodic function based on the center of the amplitude obtained by the traveling speed predicting means. Generator. 14. A signal interval calculating means for obtaining a signal interval by dividing a length of a section divided by the path dividing means by the number of signals existing in the section, wherein the acceleration / deceleration cycle predicting means comprises: The acceleration / deceleration cycle during running is corrected in accordance with the signal interval obtained by the signal interval calculating means, and the acceleration / deceleration amplitude predicting means is adjusted in accordance with the signal interval obtained by the signal interval calculating means. The traveling pattern generation device according to claim 13, wherein the deceleration amplitude is corrected. 15. A road type detecting means for detecting a road type of each of the sections divided by the route dividing means, wherein the acceleration / deceleration cycle predicting means is provided in accordance with the road type obtained by the road type detecting means. The traveling pattern generation according to claim 13, wherein the acceleration / deceleration amplitude predicting means corrects the acceleration / deceleration amplitude during traveling according to the road type obtained by the road type detecting means. apparatus. 16. A congestion degree detecting means for detecting a degree of congestion in each of the sections divided by the route dividing means, wherein the acceleration / deceleration cycle predicting means calculates a degree of congestion according to the congestion degree obtained by the congestion degree detecting means. The traveling pattern generation according to claim 13, wherein the acceleration / deceleration amplitude prediction unit corrects the acceleration / deceleration amplitude during traveling according to the congestion degree obtained by the congestion degree detection unit. apparatus. 17. A signal interval calculating means for obtaining a signal interval by dividing a length of a section divided by the path dividing means by the number of signals existing in the section, and a section divided by the path dividing means. Road type detecting means for detecting the type of road, and running speed detecting means for detecting the running speed of the vehicle. The acceleration / deceleration cycle predicting means uses the running speed obtained by the running speed detecting means. According to the signal interval obtained by the signal interval calculating means and the road type obtained by the road type detecting means, the acceleration / deceleration cycle excluding the starting acceleration from the stop and the deceleration until the stop is learned. The acceleration / deceleration amplitude prediction means uses the traveling speed obtained by the traveling speed detection means, and calculates the signal interval obtained by the signal interval calculation means and the road type detection means. 14. The running pattern generation device according to claim 13, wherein the predicted value is corrected by learning the amplitude of acceleration / deceleration excluding the starting acceleration from the stop and the deceleration until the stop, in accordance with the obtained road type. 18. The vehicle according to claim 18, further comprising a curve detecting means for detecting a curve in each of the sections divided by said route dividing means, wherein said acceleration / deceleration cycle predicting means detects a curve obtained by said curve detecting means during running. 14. The traveling pattern generation device according to claim 13, wherein the deceleration cycle is corrected, and the acceleration / deceleration amplitude prediction means corrects the acceleration / deceleration amplitude during traveling on the curve obtained by the curve detection means. 19. An energy consumption calculating means for calculating the energy consumption of the own vehicle on the running route to the destination based on the running pattern of the own vehicle generated by said running pattern generating means. Or the running pattern generation device according to 13. 20. An energy balance schedule for calculating an energy accumulation and release schedule for the energy storage means of the own vehicle in a running route to a destination based on the running pattern of the own vehicle generated by the running pattern generating means. 5. The method according to claim 4, further comprising a calculating unit.
Or the running pattern generation device according to 13.